How Google DeepMind Interviews in 2026: Process, Questions & What They Score

1. 01

   Implement merge intervals. Then a variant: given streaming intervals, maintain the merged set efficiently.

   Tests interval sorting and merging, then incremental updates to a merged set using an ordered structure.

2. 02

   Implement gradient descent for logistic regression from scratch (numpy, no scikit). Discuss convergence, learning rate, and batch vs SGD.

   Tests implementing logistic regression gradient descent in numpy, with learning rate, convergence and batch versus SGD tradeoffs.

3. 03

   Implement a piece of an ML pipeline by hand with no libraries and no AI tools, for example attention, a loss function, a sampling routine, a data pipeline or a training loop.

   Tests from-scratch ML implementation of attention, losses, sampling or training loops with no libraries or AI help.

4. 04

   Write a module to process multimodal data, with attention to scalability and performance testing.

   Tests designing a data-processing module with scalability in mind and a plan for performance testing.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   How could user feedback be leveraged for model optimization in recommendation systems?

   Tests using feedback signals to improve a recommender: training objectives, ranking, bias and feedback loops.

2. 02

   How do you handle test set contamination?

   Tests evaluation integrity: detecting and preventing benchmark data leaking into training sets.

3. 03

   How do you ensure reproducibility?

   Tests experiment reproducibility: seeds, versioned code and data, config tracking and deterministic pipelines.

4. 04

   We need to train a 100 billion parameter model, but it will not fit in memory. How would you design data and model parallelism?

   Tests distributed training design: data, model and pipeline parallelism and memory tradeoffs for a 100B model.

5. 05

   Design a scalable recommender system handling billions of items, covering candidate generation, ranking, retrieval and A/B testing.

   Tests recommender architecture: candidate generation, ranking, retrieval stages and A/B testing at billions of items.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Review this broken or suboptimal code as you would a pull request, covering correctness, performance and clarity of feedback.

   Tests code review skill: finding bugs, spotting performance problems and giving clear, respectful feedback.

2. 02

   Design a global API for Gemini.

   Tests global API design: multi-region serving, latency, scaling and reliability for a model endpoint.

3. 03

   Design a serving system for a 100B-parameter model at scale.

   Tests large-model inference design: model sharding, batching, latency, throughput and cost at scale.

4. 04

   Build an evaluation pipeline that runs thousands of benchmarks nightly.

   Tests batch pipeline design: scheduling, scale-out, result storage and reliability for nightly benchmark runs.

5. 05

   Design a feature store for an ML experimentation platform.

   Tests ML platform design: online and offline feature serving, consistency and versioning.

6. 06

   A distributed training workload runs across hundreds of machines and one worker fails several hours into the job. How do you handle it?

   Tests fault tolerance in distributed training: checkpointing, retries and recovery after a worker fails.

7. 07

   Design a platform that lets internal teams submit computationally expensive AI experiments and monitor their execution, recovering gracefully from infrastructure failures.

   Tests job-platform design: experiment submission, scheduling, execution monitoring and failure recovery.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   What's the difference between L1 and L2 regularization? When would you use one vs the other? Walk through the geometry.

   Tests regularization theory: L1 sparsity versus L2 shrinkage, explained through constraint-region geometry.

2. 02

   Walk through reinforcement learning fundamentals: MDPs, value functions, policy gradient, and the pitfalls of reward shaping.

   Tests RL theory: MDP formalism, value functions, policy gradients and how reward shaping can distort the learned policy.

3. 03

   Explain quaternions vs Euler angles for 3D rotation. What is gimbal lock? When does it occur?

   Tests 3D rotation math: quaternion versus Euler-angle representations and why gimbal lock occurs.

4. 04

   Math and theory round: expect questions on gradient computation, KL divergence properties, sampling algorithms and convergence proofs.

   Tests probability and optimization theory: gradient derivation, KL properties, sampling methods and convergence arguments.

5. 05

   Explain the difference between supervised and unsupervised learning.

   Tests ML basics: labeled versus unlabeled data, learning objectives and typical algorithms for each.

6. 06

   What are the key differences between gradient descent and stochastic gradient descent?

   Tests optimization basics: full-batch versus stochastic updates, gradient noise, per-step cost and convergence.

7. 07

   Explain the concept of overfitting and how to mitigate it in machine learning models.

   Tests generalization: spotting overfitting and fixing it with regularization, more data, early stopping or simpler models.

8. 08

   What is the importance of Markov Decision Processes (MDPs) in reinforcement learning?

   Tests RL foundations: states, actions, transitions and rewards, and why the Markov property makes the problem tractable.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Present and defend a recent paper you choose: methodology, weaknesses, alternative approaches and how you would extend it.

   Tests depth on one paper: methodology, weaknesses, alternative approaches and extensions under probing.

2. 02

   Frame an open-ended research problem the interviewer poses: propose how you would investigate it.

   Tests turning an open-ended problem into a research plan with hypotheses, experiments and evaluation.

3. 03

   Discuss your most impactful research project. What was the methodology? What are the limitations? What would you change?

   Tests depth and ownership of your own research: methodology choices, limitations and what you would change.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Why DeepMind specifically (vs OpenAI, Anthropic, Google Research)?

2. 02

   Why do you want to work at Google DeepMind?

3. 03

   What recent DeepMind paper or system have you engaged with substantively?

4. 04

   How do you handle disagreements in a team setting?

5. 05

   Describe a project where your AI solution failed and how you addressed it.

6. 06

   Share an experience where you solved a complex problem collaboratively.