# Complete Machine Learning Interview Preparation Guide

> **More from this repo**: [All guides](./README.md) | [Latest company questions](./FAANG-Recent-Questions.md) | [AI labs](./AI-Companies-Interview-Questions.md) | [System design](./SYSTEM_DESIGN_INTERVIEW.md) | [Blind 75](./Blind-75.md) | [NeetCode 150](./NeetCode-150.md)

A comprehensive collection of machine learning interview questions with detailed explanations.

## Table of Contents

- [Essential Machine Learning Theory for Interviews](#essential-machine-learning-theory-for-interviews)
- [Deep Learning Concepts from Basic to Advanced](#deep-learning-concepts-from-basic-to-advanced)
- [LLM and Generative AI Interview Questions](#llm-and-generative-ai-interview-questions)
- [RAG, Agents and Context Engineering](#rag-agents-and-context-engineering)
- [LLM Evaluation and Guardrails](#llm-evaluation-and-guardrails)
- [LLM Inference, Serving and Cost Optimization](#llm-inference-serving-and-cost-optimization)
- [Distributed Training and Scaling](#distributed-training-and-scaling)
- [Natural Language Processing Interview Questions](#natural-language-processing-interview-questions)
- [Computer Vision Interview Questions](#computer-vision-interview-questions)
- [Reinforcement Learning for ML Interviews](#reinforcement-learning-for-ml-interviews)
- [MLOps and Model Deployment Interview Topics](#mlops-and-model-deployment-interview-topics)
- [ML System Design Questions and Strategies](#ml-system-design-questions-and-strategies)
- [Statistics & Mathematics for ML Interviews](#statistics--mathematics-for-ml-interviews)
- [FAANG and Top Tech Companies' ML Interview Process](#faang-and-top-tech-companies-ml-interview-process)
- [Learning Resources for ML Interview Preparation](#learning-resources-for-ml-interview-preparation)

## Essential Machine Learning Theory for Interviews

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| What's the difference between supervised, unsupervised, and reinforcement learning? | [Answer](https://developers.google.com/machine-learning/intro-to-ml/what-is-ml) | Easy |
| Explain the bias-variance tradeoff. | [Answer](https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/) | Medium |
| What is overfitting and how do you combat it? | [Answer](https://aws.amazon.com/what-is/overfitting/) | Easy |
| Compare L1 and L2 regularization. | [Answer](https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-ridge-lasso-regression-python/) | Medium |
| Explain precision, recall, F1-score, and ROC-AUC. | [Answer](https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc) | Medium |
| What is cross-validation and why is it important? | [Answer](https://towardsdatascience.com/cross-validation-in-machine-learning-72924a69872f) | Easy |
| Explain the difference between bagging and boosting. | [Answer](https://quantdare.com/what-is-the-difference-between-bagging-and-boosting/) | Medium |
| How do decision trees work? Explain Random Forests and Gradient Boosting. | [Answer](https://towardsdatascience.com/decision-trees-and-random-forests-df0c3123f991) | Medium |
| What is feature selection and why is it important? | [Answer](https://machinelearningmastery.com/an-introduction-to-feature-selection/) | Medium |
| How does PCA work? When would you use it? | [Answer](https://builtin.com/data-science/step-step-explanation-principal-component-analysis) | Hard |

## Deep Learning Concepts from Basic to Advanced

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| Explain the architecture of a neural network. | [Answer](https://www.ibm.com/cloud/learn/neural-networks) | Easy |
| What are activation functions? Compare sigmoid, tanh, ReLU, Leaky ReLU, and softmax. | [Answer](https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0) | Medium |
| What is backpropagation? | [Answer](https://medium.com/@14prakash/back-propagation-is-very-simple-who-made-it-complicated-97b794c97e5c) | Hard |
| Explain the vanishing/exploding gradient problem. | [Answer](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) | Hard |
| What is dropout and why is it used? | [Answer](https://medium.com/@amarbudhiraja/https-medium-com-amarbudhiraja-learning-less-to-learn-better-dropout-in-deep-machine-learning-74334da4bfc5) | Medium |
| What are optimizers? Compare SGD, Adam, RMSprop, etc. | [Answer](https://towardsdatascience.com/optimizers-for-training-neural-network-59450d71caf6) | Medium |
| Explain batch normalization. | [Answer](https://d2l.ai/chapter_convolutional-modern/batch-norm.html) | Hard |
| What is transfer learning and how is it useful? | [Answer](https://machinelearningmastery.com/transfer-learning-for-deep-learning/) | Medium |
| Explain the architecture of CNNs. | [Answer](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53) | Medium |
| What are LSTMs and GRUs? How do they solve the vanishing gradient problem? | [Answer](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) | Hard |

## LLM and Generative AI Interview Questions

Most differentiating weight in 2026 AI/ML loops sits here rather than in classical ML. Classical ML is now treated as table stakes.

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| Walk through the Transformer architecture end to end. | [Attention Is All You Need](https://arxiv.org/abs/1706.03762) | Medium |
| Explain self-attention, and why it is scaled by the square root of the key dimension. | [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) | Medium |
| What is the difference between encoder-only, decoder-only and encoder-decoder models? | [Hugging Face LLM guide](https://huggingface.co/docs/transformers/main/en/llm_tutorial) | Medium |
| How do rotary position embeddings work, and why did they replace absolute embeddings? | [RoFormer / RoPE](https://arxiv.org/abs/2104.09864) | Hard |
| Explain multi-head, multi-query and grouped-query attention, and the tradeoff each makes. | [Attention Is All You Need](https://arxiv.org/abs/1706.03762) | Hard |
| What is a Mixture of Experts model, and why route to only a couple of experts per token? | [Mixtral of Experts](https://arxiv.org/abs/2401.04088) | Hard |
| Explain the compute-optimal scaling laws and what they changed about model training. | [Chinchilla](https://arxiv.org/abs/2203.15556) | Hard |
| Compare full fine-tuning, LoRA and QLoRA. When would you pick each? | [LoRA](https://arxiv.org/abs/2106.09685), [QLoRA](https://arxiv.org/abs/2305.14314) | Medium |
| Explain RLHF end to end: reward model, policy optimisation, and where it breaks. | [InstructGPT](https://arxiv.org/abs/2203.02155) | Hard |
| Compare DPO with PPO-based RLHF. Why has DPO become common? | [Direct Preference Optimization](https://arxiv.org/abs/2305.18290) | Hard |
| What is Constitutional AI and how does it reduce dependence on human labels? | [Constitutional AI](https://arxiv.org/abs/2212.08073) | Hard |
| Why does chain-of-thought prompting improve reasoning, and when does it not? | [Chain-of-Thought Prompting](https://arxiv.org/abs/2201.11903) | Medium |
| What causes hallucination, and which mitigations actually work in production? | [RAG](https://arxiv.org/abs/2005.11401) | Hard |
| Explain catastrophic forgetting during fine-tuning and how to limit it. | [LoRA](https://arxiv.org/abs/2106.09685) | Hard |

## RAG, Agents and Context Engineering

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| Design a RAG pipeline end to end, from ingestion through to a cited answer. | [RAG](https://arxiv.org/abs/2005.11401) | Medium |
| Compare dense, sparse (BM25) and hybrid retrieval. When does BM25 still win? | [RAG](https://arxiv.org/abs/2005.11401) | Medium |
| What chunking strategy would you choose, and how does chunk size affect recall? | [RAG](https://arxiv.org/abs/2005.11401) | Medium |
| Why add a re-ranker after retrieval, and what does it cost you? | [RAG](https://arxiv.org/abs/2005.11401) | Medium |
| When is fine-tuning the right answer instead of RAG? | [LoRA](https://arxiv.org/abs/2106.09685) | Medium |
| Explain the ReAct loop and where agents typically fail. | [ReAct](https://arxiv.org/abs/2210.03629) | Hard |
| How do models learn to call tools, and how do you keep tool schemas maintainable? | [Toolformer](https://arxiv.org/abs/2302.04761) | Hard |
| What is the Model Context Protocol and what problem does it solve? | [MCP](https://modelcontextprotocol.io/) | Medium |
| When should a workflow be a single agent, and when a multi-agent system? | [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) | Hard |
| Design memory for an agent: working, episodic and semantic. | [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) | Hard |
| How do you classify and gate risky agent actions such as irreversible writes? | [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) | Hard |
| What is prompt injection, and how do you defend an agent that browses the web? | [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) | Hard |

## LLM Evaluation and Guardrails

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| How do you evaluate an LLM feature that has no single correct answer? | [OpenAI Evals guide](https://platform.openai.com/docs/guides/evals) | Hard |
| Build an eval suite for a customer who has never measured quality. | [OpenAI Evals guide](https://platform.openai.com/docs/guides/evals) | Hard |
| What are the failure modes of LLM-as-a-judge, and how do you correct for them? | [OpenAI Evals guide](https://platform.openai.com/docs/guides/evals) | Hard |
| How do you measure retrieval quality separately from generation quality? | [RAG](https://arxiv.org/abs/2005.11401) | Medium |
| Explain benchmark contamination and how you would detect it. | [MMLU](https://arxiv.org/abs/2009.03300) | Hard |
| How would you run an A/B test to show model A beats model B for your users? | [OpenAI Evals guide](https://platform.openai.com/docs/guides/evals) | Medium |
| Design a red-teaming process for a model you are about to ship. | [Constitutional AI](https://arxiv.org/abs/2212.08073) | Hard |

## LLM Inference, Serving and Cost Optimization

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| What is the KV cache, why does it dominate inference memory, and how do you manage it? | [vLLM and PagedAttention](https://arxiv.org/abs/2309.06180) | Hard |
| Explain PagedAttention and the problem it solves. | [vLLM and PagedAttention](https://arxiv.org/abs/2309.06180) | Hard |
| How does FlashAttention speed up attention without changing the maths? | [FlashAttention](https://arxiv.org/abs/2205.14135) | Hard |
| Explain speculative decoding and when it stops paying off. | [Speculative Decoding](https://arxiv.org/abs/2211.17192) | Hard |
| Compare continuous batching with static batching for a serving system. | [vLLM docs](https://docs.vllm.ai/en/latest/) | Hard |
| Compare int8, int4, GPTQ and AWQ quantization, and the quality you give up. | [GPTQ](https://arxiv.org/abs/2210.17323) | Hard |
| You must cut serving cost by half without hurting quality. What do you try, in order? | [vLLM docs](https://docs.vllm.ai/en/latest/) | Hard |
| Explain the tradeoff between time to first token and total throughput. | [vLLM docs](https://docs.vllm.ai/en/latest/) | Medium |

## Distributed Training and Scaling

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| Compare data, tensor, pipeline and sequence parallelism. | [ZeRO](https://arxiv.org/abs/1910.02054) | Hard |
| Explain ZeRO sharding stages 1 to 3 and what each one saves. | [ZeRO](https://arxiv.org/abs/1910.02054) | Hard |
| How would you shard a 200B-parameter model across thousands of accelerators? | [ZeRO](https://arxiv.org/abs/1910.02054) | Hard |
| What are pipeline bubbles and how do you reduce them? | [ZeRO](https://arxiv.org/abs/1910.02054) | Hard |
| How do you handle a node failure eight hours into a training run? | [ZeRO](https://arxiv.org/abs/1910.02054) | Hard |
| Your loss spikes at step 40k. How do you debug it? | [Chinchilla](https://arxiv.org/abs/2203.15556) | Hard |
| Explain mixed-precision training and why bf16 is usually preferred over fp16. | [ZeRO](https://arxiv.org/abs/1910.02054) | Medium |

## Natural Language Processing Interview Questions

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| What is word embedding? Explain Word2Vec, GloVe. | [Answer](https://d2l.ai/chapter_natural-language-processing-pretraining/index.html) | Medium |
| Explain the Transformer architecture. | [Answer](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html) | Hard |
| How does BERT work? | [Answer](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) | Hard |
| What is attention mechanism in NLP? | [Answer](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) | Hard |
| How would you handle text preprocessing for NLP tasks? | [Answer](https://towardsdatascience.com/nlp-text-preprocessing-a-practical-guide-and-template-d80874676e79) | Easy |
| Explain how GPT models work. | [Answer](https://jalammar.github.io/illustrated-gpt2/) | Hard |
| What is beam search in sequence generation? | [Answer](https://d2l.ai/chapter_recurrent-modern/beam-search.html) | Medium |
| How do you evaluate NLP models? | [Answer](https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213) | Medium |
| Explain ROUGE, BLEU, and METEOR metrics. | [Answer](https://medium.com/explorations-in-language-and-learning/metrics-for-nlg-evaluation-c89b6a781054) | Medium |
| What are subword tokenization methods? Compare BPE, WordPiece, and SentencePiece. | [Answer](https://huggingface.co/docs/transformers/tokenizer_summary) | Medium |

## Computer Vision Interview Questions

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| Explain the architecture of a CNN. | [Answer](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53) | Medium |
| What are the different types of CNN layers? | [Answer](https://machinelearningmastery.com/convolutional-layers-for-deep-learning-neural-networks/) | Medium |
| How does object detection work? Explain YOLO, R-CNN, Fast R-CNN, Faster R-CNN. | [Answer](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) | Hard |
| What is transfer learning in computer vision? | [Answer](https://machinelearningmastery.com/how-to-use-transfer-learning-when-developing-convolutional-neural-network-models/) | Medium |
| Explain image segmentation. | [Answer](https://nanonets.com/blog/semantic-image-segmentation-2020/) | Medium |
| How does a GAN work? | [Answer](https://jonathan-hui.medium.com/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09) | Hard |
| What is data augmentation and why is it important in CV? | [Answer](https://d2l.ai/chapter_computer-vision/image-augmentation.html) | Easy |
| Explain ResNet and the concept of skip connections. | [Answer](https://towardsdatascience.com/an-overview-of-resnet-and-its-variants-5281e2f56035) | Medium |
| How do you handle class imbalance in image classification? | [Answer](https://developers.google.com/machine-learning/crash-course/overfitting/imbalanced-datasets) | Medium |
| What is the difference between semantic segmentation, instance segmentation, and panoptic segmentation? | [Answer](https://www.v7labs.com/blog/panoptic-segmentation-guide) | Medium |

## Reinforcement Learning for ML Interviews

Classical RL (Q-learning, DQN, actor-critic) still appears as a warm-up, but in 2026 AI-lab loops the RL questions that carry weight are about LLM post-training: reward models, PPO versus DPO versus GRPO, verifiable rewards and reward hacking. The rows at the bottom of this table cover that.

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| What is reinforcement learning? | [Answer](https://www.geeksforgeeks.org/what-is-reinforcement-learning/) | Easy |
| Explain the exploration-exploitation tradeoff. | [Answer](https://medium.com/online-learning-with-reinforcement-learning/understanding-the-exploration-exploitation-trade-off-in-reinforcement-learning-629bf9170f) | Medium |
| What is the difference between policy-based and value-based RL? | [Answer](https://stats.stackexchange.com/questions/512440/what-is-the-difference-between-value-based-and-policy-based-reinforcement-learni) | Medium |
| Explain Q-learning. | [Answer](https://huggingface.co/learn/deep-rl-course/unit2/introduction) | Medium |
| What is Deep Q Network (DQN)? | [Answer](https://medium.com/@jonathan_hui/rl-dqn-deep-q-network-e207751f7ae4) | Hard |
| Explain Policy Gradient methods. | [Answer](https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html) | Hard |
| What is Actor-Critic architecture? | [Answer](https://medium.com/deeplearningmadeeasy/advantage-actor-critic-a2c-algorithm-840d3551a0d9) | Hard |
| What are the challenges in reinforcement learning? | [Answer](https://arxiv.org/abs/1904.12901) | Medium |
| Explain the difference between on-policy and off-policy learning. | [Answer](https://spinningup.openai.com/en/latest/spinningup/rl_intro2.html) | Medium |
| What is Proximal Policy Optimization (PPO)? | [Answer](https://medium.com/@jonathan_hui/rl-proximal-policy-optimization-ppo-explained-77f014ec3f12) | Hard |
| How is a reward model trained from pairwise human preferences, and why does over-optimizing against it degrade the policy? | [Scaling Laws for Reward Model Overoptimization](https://arxiv.org/abs/2210.10760), [RLHF Book](https://rlhfbook.com/) | Hard |
| Walk through PPO-based RLHF for a language model: rollouts, reward, KL penalty against the reference model, value head. Where does it break? | [Illustrating RLHF](https://huggingface.co/blog/rlhf) | Hard |
| Explain GRPO. Why does it drop PPO's critic, and how is the group-relative advantage computed? | [DeepSeekMath (GRPO)](https://arxiv.org/abs/2402.03300) | Hard |
| What is RL with verifiable rewards (RLVR), and when does a rule-based verifier beat a learned reward model? | [Tulu 3](https://arxiv.org/abs/2411.15124), [DeepSeek-R1](https://arxiv.org/abs/2501.12948) | Hard |
| Compare PPO, DPO and GRPO for post-training. What does each need in data, compute and memory? | [RLHF Book](https://rlhfbook.com/) | Hard |
| What is reward hacking? Give a concrete example from LLM training and a mitigation. | [Reward Hacking in RL](https://lilianweng.github.io/posts/2024-11-28-reward-hacking/) | Medium |
| Why do RLHF and GRPO keep a KL penalty to the reference policy, and what happens if you remove it? | [RLHF Book](https://rlhfbook.com/) | Medium |

## MLOps and Model Deployment Interview Topics

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| What is MLOps? | [Answer](https://neptune.ai/blog/mlops) | Easy |
| How would you deploy a machine learning model to production? | [Answer](https://docs.cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning) | Medium |
| Explain the concept of model serving. | [Answer](https://www.datacamp.com/community/blog/machine-learning-model-serving) | Medium |
| What are the considerations for monitoring ML models in production? | [Answer](https://christophergs.github.io/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/) | Medium |
| How do you handle model drift/decay? | [Answer](https://www.evidentlyai.com/ml-in-production/concept-drift) | Medium |
| What is a feature store and why is it important? | [Answer](https://www.tecton.ai/blog/what-is-a-feature-store/) | Medium |
| How would you version control ML models? | [Answer](https://neptune.ai/blog/ml-model-versioning) | Medium |
| What is containerization and how is it useful for ML deployment? | [Answer](https://www.tensorflow.org/tfx/serving/docker) | Medium |
| Explain CI/CD in the context of ML systems. | [Answer](https://neptune.ai/blog/ci-cd-for-machine-learning-mlops) | Medium |
| What are the ethical considerations in deploying ML models? | [Answer](https://developers.google.com/machine-learning/crash-course/fairness) | Medium |

## ML System Design Questions and Strategies

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| How would you design a recommendation system? | [Answer](https://medium.com/towards-artificial-intelligence/how-to-design-the-best-recommendation-system-machine-learning-design-interview-questions-df7fc2746b90) | Hard |
| Design a large-scale image classification service. | [Answer](https://www.pluralsight.com/guides/design-a-machine-learning-system) | Hard |
| How would you build a fraud detection system? | [Answer](https://stripe.dev/blog/how-we-built-it-stripe-radar) | Hard |
| Design a chatbot system. | [Answer](https://www.ibm.com/cloud/architecture/architectures/aiChatbotArch/reference-architecture) | Hard |
| How would you design a search ranking system? | [Answer](https://arxiv.org/abs/1810.09591) | Hard |
| Design an anomaly detection system. | [Answer](https://towardsdatascience.com/machine-learning-for-anomaly-detection-and-condition-monitoring-d4614e7de770) | Hard |
| How would you build a content moderation system? | [Answer](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html) | Hard |
| Design a system for real-time bidding in online advertising. | [Answer](https://arxiv.org/abs/1610.03013) | Hard |
| How would you design a machine translation system? | [Answer](https://machinelearningmastery.com/introduction-neural-machine-translation/) | Hard |
| Design a system for dynamic pricing. | [Answer](https://en.wikipedia.org/wiki/Dynamic_pricing) | Hard |

## Statistics & Mathematics for ML Interviews

| Question | Answer Link | Difficulty |
|----------|-------------|------------|
| Explain the Central Limit Theorem. | [Answer](https://en.wikipedia.org/wiki/Central_limit_theorem) | Medium |
| What is the difference between Type I and Type II errors? | [Answer](https://en.wikipedia.org/wiki/Type_I_and_type_II_errors) | Easy |
| Explain Bayes' theorem and give an example. | [Answer](https://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/) | Medium |
| What is the difference between correlation and causation? | [Answer](https://medium.com/towards-artificial-intelligence/correlation-vs-causation-6d7d39278ed8) | Easy |
| What is an eigenvalue and eigenvector? | [Answer](https://medium.com/fintechexplained/what-are-eigenvalues-and-eigenvectors-a-must-know-concept-for-machine-learning-80d0fd330e47) | Hard |
| Explain hypothesis testing and p-values. | [Answer](https://en.wikipedia.org/wiki/P-value) | Medium |
| What is the curse of dimensionality? | [Answer](https://towardsdatascience.com/the-curse-of-dimensionality-50dc6e49aa1e) | Medium |
| Explain the difference between MLE and MAP estimation. | [Answer](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) | Hard |
| What is the difference between a PDF and CDF? | [Answer](https://en.wikipedia.org/wiki/Cumulative_distribution_function) | Medium |
| Explain the concepts of gradient descent, stochastic gradient descent, and mini-batch gradient descent. | [Answer](https://towardsdatascience.com/gradient-descent-algorithm-and-its-variants-10f652806a3) | Medium |

## FAANG and Top Tech Companies' ML Interview Process

The ML interview process at top tech companies typically spans 1.5-2.5 months and consists of multiple stages designed to thoroughly evaluate candidates' technical skills, problem-solving abilities, and cultural fit. Here's a detailed breakdown of what to expect at each company:

### Google ML Interview Process

**Process Overview:**
1. **Resume Screening**: Initial filter based on background and experience
2. **Technical Phone Screen (45-60 minutes)**:
   - Coding question (data structures & algorithms)
   - Basic ML concepts (10-15 minutes)
3. **Virtual Onsite (Full Day)**: since Pichai's August 2025 town hall, Google has been adding at least one in-person round back to many loops to curb AI-assisted cheating, so confirm the format with your recruiter
   - 2-3 Coding interviews (LeetCode medium/hard)
   - 1-2 ML Algorithm & Theory rounds
   - ML System Design round
   - Behavioral/Leadership round

**Key Focus Areas:**
- Deep understanding of ML fundamentals and algorithms
- Strong coding skills (Python preferred)
- Experience with PyTorch or JAX and with TPU/GPU training infrastructure. TensorFlow now appears mostly in legacy serving and on-device stacks
- Ability to design end-to-end ML systems
- Problem-solving in ambiguous scenarios

**Evaluation Criteria:**
- Technical depth in ML concepts
- Coding proficiency and clean implementation
- System design approach and tradeoff considerations
- Communication and collaboration skills
- Googleyness and leadership qualities. Pichai redefined Googleyness at a December 2024 all-hands as six traits: mission first, make helpful things, be bold and responsible, stay scrappy, hustle and have fun, team Google. Behavioral answers are scored against these

### Meta (formerly Facebook) ML Interview Process

**Process Overview:**
1. **Resume Screening**
2. **Initial Technical Screen (45-60 minutes)**:
   - Coding question (algorithmic problem)
   - Basic ML knowledge assessment
3. **Virtual Onsite (Full Day)**:
   - ML Fundamentals round (theory, algorithms, statistics)
   - Applied ML & Product Sense round (applying ML to Meta products)
   - ML System Design round (end-to-end system)
   - Coding round (data structures & algorithms). Since October 2025 Meta has been swapping one coding round for a 60-minute AI-enabled CoderPad round on E5 to E7 SWE and M2 loops: a multi-file codebase with failing tests, an embedded model you pick (GPT-5, Claude, Gemini or Llama), scored on problem solving, code quality, verification and communication
   - Behavioral round (using Meta's core values framework)

**Key Focus Areas:**
- Deep understanding of ML algorithms and applications
- Experience with PyTorch (preferred) and production ML
- Strong coding skills in Python
- Familiarity with distributed training and large-scale ML
- Product sense and impact-driven thinking

**Evaluation Criteria:**
- Technical problem-solving abilities
- ML system design skills
- Understanding of ML metrics and evaluation
- Communication and collaboration style
- Alignment with Meta's six values: Move Fast, Focus on Long-Term Impact, Build Awesome Things, Live in the Future, Be Direct and Respect Your Colleagues, and Meta, Metamates, Me

### Amazon ML Interview Process

**Process Overview:**
1. **Resume Screening**
2. **Initial Technical Assessment**:
   - Online coding assessment or
   - Technical phone screen (45-60 minutes)
3. **Virtual Onsite (Full Day)**:
   - 1-2 Coding interviews (data structures & algorithms)
   - ML Concepts & Fundamentals round
   - ML System Design round
   - Applied ML / Domain-specific round
   - Bar Raiser round (leadership principles focus)

**Key Focus Areas:**
- ML theory and practical implementation
- Coding proficiency in Python
- Understanding of AWS ML services (SageMaker, Bedrock)
- System design for scalable ML solutions
- Leadership principles alignment

**Evaluation Criteria:**
- Technical depth in ML algorithms
- Coding skills and problem-solving approach
- System design capabilities
- Leadership principles, 16 in total (Customer Obsession, Ownership, Invent and Simplify, Dive Deep, Have Backbone; Disagree and Commit, etc.). GenAI tools are banned during Amazon interviews unless explicitly permitted, and detected use is disqualifying
- Communication and stakeholder management

### Microsoft ML Interview Process

**Process Overview:**
1. **Resume Screening**
2. **Initial Technical Screen (45-60 minutes)**:
   - Coding question (data structures & algorithms)
   - Basic ML concepts
3. **Virtual Onsite (Full Day)**:
   - 2 Coding rounds (data structures & algorithms)
   - ML Theory & Fundamentals round
   - ML System Design round
   - Team-specific technical questions
   - Behavioral assessment ("as appropriate" round)

**Key Focus Areas:**
- Strong foundation in ML algorithms and mathematics
- Coding proficiency (Python/C#)
- Familiarity with Azure ML services: Azure Machine Learning and Microsoft Foundry (formerly Azure AI Studio and Azure AI Foundry)
- System design for ML applications
- Problem-solving and collaborative approach

**Evaluation Criteria:**
- Technical knowledge depth
- Coding skills and implementation
- System design capabilities
- Communication and collaboration
- Growth mindset and learning attitude

### OpenAI ML Interview Process

**Process Overview:**
1. **Application Review**: Rigorous screening focusing on research background (publications matter for research roles, shipped systems for engineering roles)
2. **Recruiter and Hiring Manager Screens**: motivation, mission fit, and a walk through your past projects
3. **Initial Technical Screen (60 minutes)**:
   - One coding problem that gets harder in stages (the "gate" format); candidates report needing to clear at least two gates to advance
   - ML fundamentals and research understanding for research tracks
4. **Take-Home Work Trial (some tracks)**: a practical engineering project in a 48-hour window, graded on reliability, tests and code quality rather than feature count
5. **Virtual Onsite (Multiple Rounds)**: 4 to 6 rounds, virtual or in person
   - Deep Learning Theory / ML depth round
   - Research Understanding & Paper Discussion round (research roles)
   - ML System Design or Research Design round
   - Coding interview (algorithmic and ML implementation)
   - Technical project presentation
   - Behavioral and values round: mission alignment, safety and ethical judgment are probed here rather than in a standalone ethics interview

Typical end-to-end timeline is 5 to 8 weeks.

**Key Focus Areas:**
- Deep understanding of ML research literature
- Strong mathematics and statistics foundation
- Implementation skills for ML algorithms
- Familiarity with deep learning frameworks
- Understanding of AI ethics and alignment
- Research background and publication record (for research roles)

**Evaluation Criteria:**
- Research depth and understanding
- Technical implementation abilities
- System design thinking
- Alignment with OpenAI's mission and values
- Collaborative approach to research
- Ethics and safety considerations

### Apple ML Interview Process

**Process Overview:**
1. **Resume Screening**
2. **Initial Technical Phone Screen**:
   - Coding question
   - ML fundamentals
3. **Virtual Onsite (Multiple Rounds)**:
   - 1-2 Coding interviews (algorithms and data structures)
   - ML Theory and Fundamentals round
   - ML Coding round (implementing ML algorithms)
   - ML System Design round
   - Team-specific technical questions
   - Behavioral assessment

**Key Focus Areas:**
- Strong ML theory and implementation skills
- Experience with Apple's ML stack: Core ML and Create ML for deployment, MLX for training and inference on Apple silicon, and the Foundation Models framework (WWDC 2025) for calling the on-device Apple Intelligence model
- On-device ML optimization techniques
- Privacy-preserving ML approaches: on-device inference first, Private Cloud Compute when a request needs server-side models
- Problem-solving in resource-constrained environments

**Evaluation Criteria:**
- Technical depth in ML concepts
- Coding and implementation skills
- System design approach
- Alignment with Apple's values and culture
- Communication and collaboration abilities

<a name="recent-ml-interview-questions-2023-2025"></a>

## Recent ML Interview Questions (2024-2026)

Below are actual ML interview questions recently asked at top tech companies, organized by interview round type:

<a name="ml-fundamentals--theory-questions-2025-updates"></a>

### ML Fundamentals & Theory Questions (2025-2026)

1. **Explain the bias-variance tradeoff and how it relates to model complexity.** (Google, 2023)
2. **Walk through the mathematics of backpropagation for a simple neural network.** (Meta, 2024)
3. **Compare and contrast L1 and L2 regularization, including their effects on model parameters.** (Microsoft, 2023)
4. **How would you handle class imbalance in a classification problem? Explain the tradeoffs of different approaches.** (Amazon, 2023)
5. **Explain the vanishing gradient problem in RNNs and how LSTMs/GRUs address it.** (Apple, 2024)
6. **What are the assumptions of linear regression and how would you verify them?** (Google, 2024)
7. **Explain the concept of attention mechanisms and how they work in transformer models.** (OpenAI, 2023)
8. **How would you implement early stopping? What metrics would you monitor and why?** (Meta, 2023)
9. **Explain the concept of embedding space in NLP models. How would you evaluate the quality of word embeddings?** (Microsoft, 2024)
10. **Compare and contrast different optimizers (SGD, Adam, RMSprop) and when you would use each.** (OpenAI, 2024)
11. **What is the difference between same and valid padding in CNNs? When would you use each?** (Google, 2025)
12. **Explain how transformers solve the long-range dependency problem that RNNs struggle with.** (Meta, 2025)
13. **What are the advantages and disadvantages of using attention mechanisms versus traditional sequence models?** (OpenAI, 2025)
14. **Describe the tradeoffs between model size and inference speed. How do you optimize this balance?** (Microsoft, 2025)
15. **How do you identify and handle outliers in your training data, and how might they impact different ML algorithms?** (Amazon, 2025)

<a name="ml-system-design-questions-2025-updates"></a>

### ML System Design Questions (2025-2026)

1. **Design a recommendation system for YouTube videos.** (Google, 2023)
2. **Design an ML system to detect fake accounts on Instagram.** (Meta, 2024)
3. **Design a dynamic pricing system for ride-sharing.** (Uber, 2023)
4. **Design a ranking system for search results on e-commerce platforms.** (Amazon, 2023)
5. **Design a personalized news feed ranking algorithm.** (Microsoft, 2024)
6. **Design a system to detect anomalies in payment transactions.** (Apple, 2023)
7. **Design an evaluation framework for a content moderation system.** (OpenAI, 2024)
8. **Design a system to optimize notifications to maximize user engagement while minimizing fatigue.** (Meta, 2023)
9. **Design a system to provide accurate ETA predictions for food delivery.** (DoorDash, 2024)
10. **Design a multimodal content understanding system for social media posts.** (Google, 2024)
11. **Design a real-time fraud detection system that can adapt to evolving patterns.** (Stripe, 2025)
12. **Design an AI system that can generate personalized learning content for education platforms.** (Microsoft, 2025)
13. **Design a system for automated code review and optimization using LLMs.** (GitHub/Microsoft, 2025)
14. **Design an AI assistant that can help debug software issues by analyzing logs and code.** (Google, 2025)
15. **Design a system to identify potentially harmful content in generative AI outputs.** (OpenAI, 2025)

<a name="ml-coding-questions-2025-updates"></a>

### ML Coding Questions (2025-2026)

1. **Implement a decision tree from scratch.** (Google, 2023)
2. **Write code to implement stochastic gradient descent for linear regression.** (Microsoft, 2024)
3. **Implement a function to compute the precision, recall, and F1 score for a binary classifier.** (Amazon, 2023)
4. **Code a simple neural network with backpropagation using only NumPy.** (Meta, 2024)
5. **Implement a k-means clustering algorithm from scratch.** (Apple, 2023)
6. **Write code to handle class imbalance using various sampling techniques.** (OpenAI, 2024)
7. **Implement a simple recommendation system using collaborative filtering.** (Netflix, 2023)
8. **Code a function to detect and handle outliers in a dataset.** (Microsoft, 2023)
9. **Implement regularization (L1 and L2) for linear regression from scratch.** (Google, 2024)
10. **Write code to perform cross-validation and hyperparameter tuning for a random forest model.** (Amazon, 2024)
11. **Implement a transformer encoder layer from scratch using PyTorch.** (OpenAI, 2025)
12. **Code a function that implements the focal loss for handling class imbalance.** (Meta, 2025)
13. **Create an implementation of online learning for a logistic regression model.** (Google, 2025)
14. **Implement a custom attention mechanism for a specific NLP task.** (Microsoft, 2025)
15. **Build a simple but efficient pipeline for handling time series forecasting with missing values.** (Amazon, 2025)

<a name="llm-specific-interview-questions-2025-updates"></a>

### LLM-Specific Interview Questions (2025-2026)

1. **Explain the key innovations in the transformer architecture compared to RNNs.** (OpenAI, 2023)
2. **How would you evaluate a large language model? What metrics would you use beyond perplexity?** (Google, 2024)
3. **Explain the concept of prompt engineering. How would you design prompts for different tasks?** (Meta, 2023)
4. **What approaches would you use to reduce hallucinations in LLM outputs?** (Microsoft, 2024)
5. **Explain the Reinforcement Learning from Human Feedback (RLHF) approach used in models like ChatGPT.** (OpenAI, 2024)
6. **How would you implement efficient fine-tuning for a domain-specific LLM application?** (Amazon, 2023)
7. **Design a RAG (Retrieval-Augmented Generation) system for a corporate knowledge base.** (Apple, 2024)
8. **How would you handle multilingual capabilities in large language models?** (Google, 2023)
9. **Explain the concept of model distillation and how you would apply it to LLMs.** (Meta, 2024)
10. **How would you implement and evaluate an LLM-based code generation system?** (GitHub/Microsoft, 2023)
11. **What is the difference between Tree of Thought and Chain of Thought prompting? When would you use each approach?** (OpenAI, 2025)
12. **How do you detect when a model is hallucinating and what strategies would you implement to minimize hallucinations?** (Google, 2025)
13. **Explain the recent techniques for reducing the context window requirements for transformer-based models.** (Meta, 2025)
14. **How would you implement and evaluate a multi-agent system using LLMs for complex reasoning tasks?** (Microsoft, 2025)
15. **Compare and contrast different techniques for efficient inference in LLMs (e.g., quantization, KV caching, speculative decoding).** (OpenAI, 2025)

### LLM and AI Engineering Questions (2026 Updates)

1. **How does DPO (Direct Preference Optimization) simplify RLHF? When would you choose DPO over PPO?** (Anthropic, OpenAI, 2026)
2. **What is KV cache and how does it help in LLM inference? How would you optimize KV cache reuse?** (OpenAI, Google, 2026)
3. **When do you route to a small distilled model vs. a large LLM? Design a model tiering/routing system.** (Google, Meta, 2026)
4. **What is prompt compression and how does it reduce cost?** (OpenAI, 2026)
5. **Explain multi-layer caching for GenAI: retrieval cache, prompt cache, response cache.** (Anthropic, Google, 2026)
6. **Naive RAG fails at retrieval ~40% of the time. Explain Adaptive RAG, Corrective RAG (CRAG), Self-RAG, and Cache-Augmented Generation (CAG).** (All AI companies, 2026)
7. **Explain semantic chunking vs fixed-size chunking for RAG pipelines. How do you detect topic boundaries?** (OpenAI, 2026)
8. **How do you protect against prompt injection and jailbreaking in production LLM systems?** (Anthropic, OpenAI, 2026)
9. **Your app gets 1M queries/day -- how do you optimize LLM cost? Cover token reduction, model tiering, prompt compression, and caching.** (All, 2026)
10. **How do you handle PII in prompts and logs? Describe HIPAA-specific scenarios for healthcare GenAI.** (Google, Microsoft, 2026)
11. **"Is there an actual eval framework, or is it vibes-based?" Build golden datasets, automated eval pipelines, and distinguish subjective quality from measurable metrics.** (OpenAI, 2026)
12. **Implement LoRA and QLoRA from scratch. Explain when to use each.** (NVIDIA, Meta, 2026)
13. **Design an autonomous agent architecture: orchestrator, tool gateway, memory systems, policy engine, state management, observability.** (Anthropic, OpenAI, Google, 2026)
14. **What is speculative decoding and how does it improve inference throughput?** (Google, Anthropic, 2026)
15. **Implement beam search, top-k, and top-p decoding algorithms.** (OpenAI, NVIDIA, 2026)
16. **Explain GRPO and RL with verifiable rewards. Why did reasoning-model training move from learned reward models to rule-based verifiers for math and code, and where does that stop working?** (AI labs, 2026)
17. **Walk through the RLHF pipeline and name where it usually fails: preference data quality, reward hacking, KL drift, over-refusal after safety training.** (AI labs, 2026)

<a name="mlops-questions-2025-updates"></a>

### MLOps Questions (2025-2026)

1. **How would you monitor an ML model in production? What metrics would you track?** (Google, 2023)
2. **Explain your approach to handling data drift and model decay.** (Amazon, 2024)
3. **Describe your experience with CI/CD pipelines for ML models.** (Microsoft, 2023)
4. **How would you design a feature store for a large-scale ML system?** (Meta, 2024)
5. **What strategies would you use to optimize inference latency for an ML model in production?** (Apple, 2023)
6. **How would you approach A/B testing for ML model deployments?** (Netflix, 2024)
7. **Describe your approach to ML model version control and reproducibility.** (OpenAI, 2023)
8. **How would you manage compute resources for training large models efficiently?** (Google, 2024)
9. **How would you handle model explainability requirements in a regulated industry?** (Microsoft, 2023)
10. **Explain how you would implement a multi-stage deployment strategy for ML models.** (Amazon, 2024)
11. **How would you implement a shadow deployment for a critical ML model? What metrics would you track?** (Google, 2025)
12. **Design a system for automated model monitoring that can detect and respond to various types of drift.** (Meta, 2025)
13. **How would you implement an efficient continuous training pipeline for a model that needs to be updated daily?** (Amazon, 2025)
14. **Explain your strategy for implementing model governance in an organization with multiple ML teams.** (Microsoft, 2025)
15. **How would you design an ML infrastructure that can support both experimentation and production needs efficiently?** (OpenAI, 2025)

### New Interview Formats (2026)

Major companies are transforming their ML interview process in 2026:

1. **AI-Paired Coding Rounds, where allowed**: policy varies by company, level and team, so confirm with your recruiter before every loop. Where AI is allowed you are scored on ownership, prompting, verification of the output and debugging what the model wrote, not on typing speed.

| Company | AI in coding rounds |
|---------|---------------------|
| Meta | One 60-min CoderPad round with models embedded, now reaching SWE and EM roles up through E7/M2 |
| Google | Pilot. Gemini supplied in-round on selected teams, and AI fluency is scored |
| LinkedIn | One coding round replaced by an AI-enabled round, scored 1-4 where 3 passes |
| DoorDash | A 60-min AI-assisted working session in your own IDE |
| Amazon | Prohibited. Detected use of unauthorised GenAI is disqualifying |
| Google DeepMind, ByteDance, Palantir | Prohibited in technical rounds |
| AI-native startups such as Cursor and Sierra | Allowed and expected |
2. **Reasoning Labs**: Ambiguous ML challenges with no single correct answer. Evaluates thinking clarity, uncertainty handling, and ethical awareness.
3. **AI Reasoning Challenge**: Candidates analyze AI-generated outputs (explanations, logs, summaries) and identify flaws, suggest improvements, and design verification experiments.
4. **Ethics and Safety Rounds**: Now standalone at many companies. Sample: "detecting gender bias in LLMs" or "balancing accuracy with safety in dialogue models."
5. **Dedicated MLOps Block**: A 45-minute MLOps interview testing data drift monitoring, pipeline debugging, and production reliability is now standard.

<a name="advanced-ml--ai-topics-2025-updates"></a>

### Advanced ML & AI Topics (2025-2026)

1. **Explain the differences between contrastive learning, self-supervised learning, and supervised learning.** (Google, 2025)
2. **How do diffusion models work? Describe the forward and reverse processes.** (OpenAI, 2025)
3. **What is curriculum learning and when would you apply it in training neural networks?** (Meta, 2025)
4. **Describe how model distillation works and why it's effective for creating smaller, efficient models.** (Microsoft, 2025)
5. **Explain how multimodal models fuse information from different modalities. What are the challenges?** (Google, 2025)
6. **How do you approach the development of AI systems that can reason about causality rather than just correlation?** (OpenAI, 2025)
7. **Describe the techniques used for efficient training of large models across multiple GPUs.** (Meta, 2025)
8. **What are the key challenges in developing AI systems that can plan complex actions over long time horizons?** (Microsoft, 2025)
9. **Explain how neural architecture search works and its practical applications in model design.** (Google, 2025)
10. **How do you approach the problem of building AI systems that can generalize to unseen domains?** (OpenAI, 2025)

<a name="behavioral-questions-for-ml-roles-2025-updates"></a>

### Behavioral Questions for ML Roles (2025-2026)

1. **Tell me about a time when you had to balance model accuracy with deployment constraints.** (Google, 2023)
2. **Describe a situation where you had to explain complex ML concepts to non-technical stakeholders.** (Meta, 2024)
3. **Tell me about a time when you had to make a decision with incomplete data.** (Microsoft, 2023)
4. **How have you handled disagreements with team members about model design choices?** (OpenAI, 2024)
5. **Describe a project where you had to iterate on an ML solution that wasn't working as expected.** (Amazon, 2023)
6. **Tell me about a time when you identified and solved a problem before it became critical.** (Apple, 2024)
7. **How do you stay current with the rapidly evolving field of machine learning?** (Google, 2023)
8. **Describe a situation where you had to prioritize between multiple competing ML projects.** (Meta, 2023)
9. **Tell me about a time when you had to consider ethical implications in an ML project.** (Microsoft, 2024)
10. **How have you incorporated feedback to improve your ML models or approaches?** (OpenAI, 2023)
11. **Describe a situation where you had to balance innovation with practical implementation in an ML project.** (Google, 2025)
12. **Tell me about a time when you had to advocate for a more complex ML approach against simpler alternatives.** (Meta, 2025)
13. **How have you collaborated with cross-functional teams to deliver an end-to-end ML solution?** (Amazon, 2025)
14. **Describe a situation where you had to make a tradeoff between model performance and fairness/ethical considerations.** (Microsoft, 2025)
15. **Tell me about a time when you had to pivot your ML approach based on new information or constraints.** (OpenAI, 2025)

## Interview Preparation Tips

### Technical Preparation

1. **Review ML Fundamentals**: Ensure strong understanding of core ML concepts, algorithms, and mathematics.
2. **Practice Coding**: Work through ML-specific coding problems and implementations.
3. **System Design Practice**: Develop a framework for approaching ML system design questions.
4. **Stay Current**: Read recent papers and understand state-of-the-art approaches, especially in your specialty area.
5. **Understand MLOps**: Familiarize yourself with ML deployment, monitoring, and lifecycle management.

### Interview Strategy

1. **Clarify Requirements**: Always start by asking clarifying questions to understand the problem scope.
2. **Structured Approach**: Use a clear framework for system design and problem-solving questions.
3. **Think Aloud**: Share your thought process throughout the interview.
4. **Consider Tradeoffs**: Explicitly discuss pros and cons of different approaches.
5. **Connect to Real Experience**: Relate questions to your past work where relevant.

### Company-Specific Preparation

1. **Research Products**: Understand the company's ML applications and products.
2. **Technical Blog Posts**: Read company engineering blogs to understand their ML approaches.
3. **Cultural Values**: Familiarize yourself with company values and leadership principles.
4. **Recent Innovations**: Be aware of the company's recent ML research or product launches.
5. **Prepare Questions**: Have thoughtful questions ready about team, projects, and growth opportunities.

## Learning Resources for ML Interview Preparation

### Books for ML Interview Prep

- [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)
- [Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville](https://www.deeplearningbook.org/)
- [Pattern Recognition and Machine Learning by Christopher Bishop](https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/)
- [Machine Learning Yearning by Andrew Ng](https://info.deeplearning.ai/machine-learning-yearning-book)
- [The Hundred-Page Machine Learning Book by Andriy Burkov](http://themlbook.com/)

### Top Online Courses for ML Interviews

- [Machine Learning by Andrew Ng (Stanford/Coursera)](https://www.coursera.org/learn/machine-learning)
- [Deep Learning Specialization (Coursera)](https://www.coursera.org/specializations/deep-learning)
- [CS224n: Natural Language Processing with Deep Learning (Stanford)](http://web.stanford.edu/class/cs224n/)
- [CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)](http://cs231n.stanford.edu/)
- [Fast.ai Practical Deep Learning for Coders](https://www.fast.ai/)
- [Full Stack Deep Learning](https://fullstackdeeplearning.com/)

### ML Interview Preparation Platforms

- [InterviewQuery](https://www.interviewquery.com/)
- [LeetCode (ML Section)](https://leetcode.com/explore/learn/card/machine-learning-101/)
- [Machine Learning Mastery](https://machinelearningmastery.com/)
- [Educative.io ML Interview Prep Course](https://www.educative.io/courses/grokking-the-machine-learning-interview)
- [StrataScratch](https://www.stratascratch.com/)

### ML Communities for Interview Questions

- [Kaggle](https://www.kaggle.com/)
- [ML subreddit](https://www.reddit.com/r/MachineLearning/)
- [Hugging Face Papers](https://huggingface.co/papers/trending) (Papers With Code was shut down in July 2025 and its domain now redirects here)
- [AI Alignment Forum](https://www.alignmentforum.org/)
- [ML Collective](https://mlcollective.org/)

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