78 traps set in interviews at Google DeepMind. Each one: the interviewer's question, the answer most candidates give, and the mechanism that breaks it.
Roles: Senior AI Engineer (25), Senior ML Engineer (15), Senior Machine Learning Engineer (6), Senior Computer Vision Engineer (5), AI Engineer (3).
From LLM System Design Interview, Advanced Deep Learning Interview Questions, LLM Agents Interview Questions, Advanced NLP Interview Questions, Advanced Reinforcement Learning Interview Questions, Computer Vision Interview Questions, Machine Learning System Design Interview, LLM Inference Interview Questions, Generative Vision Interview Questions, AI Agent Engineering Interview and RAG Interview Questions.
Each trap is set in an interview at Google DeepMind. AI Interview Prep isn't affiliated with Google DeepMind.
Senior AI Engineer interview at Google DeepMind
“You swap your agent’s backbone from DeepSeek to Qwen to cut costs. Same prompts, same tool schemas. Suddenly tool calls silently stop executing. No errors, no crashes. What broke?”
Agent Engineering #3
Sep 22, 2026
Senior ML Engineer interview at Google DeepMind
“You switched to GRPO. Average chain-of-thought length grows every training step. Leadership calls it ‘the model learning to think harder.’ What’s the less flattering explanation, and how do you verify it before your inference bill doubles?”
LLM System Design #73
Sep 17, 2026
Senior AI Engineer interview at Google DeepMind
“Your VP says: ‘We scraped the web in 2020 and got a great corpus. Just re-run the crawler and we’ll get the same data, only bigger.’ Why is that wrong, and how does it change your pre-training data strategy?”
LLM System Design #67
Sep 11, 2026
Senior AI Engineer interview at Google DeepMind
“Your agent retrieves mid-reasoning instead of once upfront. Recall@10 went up. Answer accuracy went down. Where’s the failure?”
LLM Inference #23
Aug 23, 2026
Senior ML Engineer interview at Google DeepMind
“Your colleague says greedy decoding is a fine approximation of the most likely sequence, because we take the max at every step. Where does that argument break, and does beam search actually fix it?”
LLM Inference #21
Aug 21, 2026
Staff ML Engineer interview at Google DeepMind
“AlphaCode picked submissions by clustering programs on behavioral equivalence. AlphaCode 2 used a fine-tuned scoring model instead. Your junior wants to rip out the clustering and ship the reward model tomorrow. What breaks?”
LLM Inference #20
Aug 20, 2026
Senior AI Engineer interview at Google DeepMind
“You 10x’d your sampling budget on a hard reasoning task and the solve rate barely moved, even though the paper’s log-linear scaling curve says it should have. What’s the first thing you measure?”
LLM Inference #19
Aug 19, 2026
ML Engineer interview at Google DeepMind
“Your GraphRAG system uses a generic related_to edge to catch messy connections. Recall jumped. Six months later, retrieval quality is quietly rotting. What went wrong?”
RAG #22
Jul 26, 2026
Research Engineer interview at Google DeepMind
“Your 1000-step diffusion teacher is too slow for production, so you distill it into a single-step student. The output is garbage. You swap in a bigger student. Still garbage. What’s actually broken?”
Generative Vision #24
Jul 3, 2026
Senior GenAI Engineer interview at Google DeepMind
“How does Conditional Flow Matching bypass the intractability of learning the marginal vector field?”
Generative Vision #8
Jun 16, 2026
Senior AI Engineer interview at Google DeepMind
“In continuous generative models, what is the exact physical difference between a ‘score’ and a ‘vector field’?”
Generative Vision #7
Jun 15, 2026
Senior ML Engineer interview at Google DeepMind
“We’re deploying a stateful neural network backbone that continuously updates on a non-stationary data stream. Our standard cross-entropy loss is massively accelerating catastrophic forgetting. What alternative representational objective do you implement to stop the bleeding, and why?”
ML System Design #49
Jun 6, 2026
Senior ML Engineer interview at Google DeepMind
“You deploy Elastic Weight Consolidation (EWC) to fix catastrophic forgetting during continual fine-tuning. The model successfully retains its historical accuracy, but its adaptation to the new domain completely stalls. Why?”
ML System Design #47
Jun 4, 2026
Senior AI Engineer interview at Google DeepMind
“You deployed a 70B target model with a 1B draft model for speculative decoding. Accuracy is identical, but your expected 2x speedup is sitting at exactly 0%. Why?”
LLM System Design #50
May 13, 2026
Senior Pre-training Engineer interview at Google DeepMind
“You’ve trained a series of smaller proxy models to project scaling laws for your next 100B+ flagship LLM. However, your parameter-to-loss plot isn’t a straight line in log-log space, it’s bending noticeably at the low-parameter end. Assuming training was perfectly stable, what basic structural miscalculation is ruining your extrapolation curve?”
LLM System Design #49
May 12, 2026
Senior AI Engineer interview at Google DeepMind
“You scaled your pre-training dataset by 10x, but the error rate barely budged. Your model is massively over-parameterized, so capacity isn’t the issue. What ‘intrinsic’ statistical property of your target task is fundamentally bottlenecking your power-law returns?”
LLM System Design #48
May 11, 2026
Senior Pre-training Engineer interview at Google DeepMind
“Compute is tight, but you need to find the exact optimal ratio of code, web, and book data for a 100B parameter model. How do you empirically determine the perfect mixture without wasting millions of dollars running ablation tests at massive scale?”
LLM System Design #47
May 10, 2026
Senior AI Engineer interview at Google DeepMind
“You aggressively cast your entire model to Float16 to double your memory bandwidth and halve your payload. It runs blazingly fast, but your loss diverges and produces NaNs immediately. What critical separation of concerns did you fail to implement in your arithmetic intensity strategy?”
LLM System Design #44
May 7, 2026
Senior AI Engineer interview at Google DeepMind
“Your training job is unacceptably slow, so you secure the budget to upgrade to a new cluster with 5x the raw teraFLOPs. However, your end-to-end throughput barely increases by 1.2x. What fundamental hardware scaling reality did you fail to profile before upgrading?”
LLM System Design #42
May 5, 2026
Senior ML Engineer interview at Google DeepMind
“Sending the exact same prompt yields slightly different outputs depending on the time of day.”
LLM System Design #40
May 3, 2026
Senior AI Engineer interview at Google DeepMind
“You are pre-training a massive Mixture of Experts (MoE) model on a cluster of H100s. Suddenly, you hit catastrophic loss spikes that completely derail convergence. Your team panics and suggests drastically lowering the learning rate from 1e-4 or aggressively tightening gradient clipping. You look at the MoE router layer and veto both ideas. Why?”
LLM System Design #39
May 2, 2026
Senior AI Engineer interview at Google DeepMind
“You deployed a top-K routed MoE. Within 500 steps, validation loss flatlines. You check the telemetry and see 6 of your 8 experts have near-zero activation. A junior MLE proposes injecting stochastic jitter to the router logits to force exploration. Do you merge the PR?”
LLM System Design #38
May 1, 2026
Senior LLM Pre-training Engineer interview at Google DeepMind
“We are setting up a single-epoch pre-training run on a 10-petabyte text corpus. A junior engineer removes the weight decay, arguing we can’t possibly overfit a dataset this large in one epoch. Do you merge their PR?”
LLM System Design #37
Apr 30, 2026
Senior LLM Engineer interview at Google DeepMind
“You inherit a legacy Transformer codebase and notice the team is dropping all bias terms in the linear layers. Someone suggests adding them back to increase the representational power of the model. What production-level training catastrophe are you actively inviting if you approve that pull request?”
LLM System Design #35
Apr 28, 2026
Senior ML Engineer interview at Google DeepMind
“You are passing a transposed matrix into a custom loss function to fix a shape mismatch. Your code throws a contiguity error, so you add a .reshape() or .contiguous().view() to make it compile. Suddenly, your PyTorch profiler shows a massive, unexpected spike in VRAM usage. What silent memory allocation trap did you just trigger?”
LLM System Design #31
Apr 21, 2026
Senior Generative AI Engineer interview at Google DeepMind
“Your tech lead insists on using a heavily optimized GAN for a new pipeline because of its blazing fast inference and crisp quality. But the enterprise client requires capturing the absolute full, long-tail diversity of the training dataset. Why is your tech lead about to ruin the project?”
Deep Learning #25
Apr 15, 2026
Senior Computer Vision Engineer interview at Google DeepMind
“In staging, your GAN produces stunning, photorealistic faces, but QA reports that every generated face looks like the exact same three people. Why is your highly optimized discriminator completely blind to this severe mode collapse, and what specific architectural change must you make to penalize this behavior at the batch level?”
Deep Learning #23
Apr 13, 2026
Senior Machine Learning Engineer interview at Google DeepMind
“Your newly initialized standard GAN is suffering from vanishing gradients right out of the gate, and the discriminator’s accuracy is sitting at exactly 100%. How do you fix it?”
Deep Learning #22
Apr 12, 2026
Senior Computer Vision Engineer interview at Google DeepMind
“We are passing high-resolution medical images through a deep, 50-layer CNN. To save VRAM on our H100 GPUs, a junior proposes dropping zero-padding on all convolutions, arguing we only lose a tiny 2-pixel border per layer. Do you approve this PR?”
Deep Learning #21
Apr 11, 2026
Senior ML Engineer interview at Google DeepMind
“You’ve implemented a custom 1D convolutional layer from scratch for specialized edge hardware. During training, the loss plateaus immediately, and the filters completely fail to learn translation invariance. Assuming your forward pass and chain rule math are perfect, what critical gradient aggregation step did you likely forget to apply to the shared weights before updating?”
Deep Learning #17
Apr 7, 2026
Senior Machine Learning Engineer interview at Google DeepMind
“Your deep neural network achieves near-zero training loss but outputs absolute garbage in production. You plot it and see the network has learned a jagged, highly complex function perfectly threading a needle through your sparse training points. How does Early Stopping physically prevent the network from molding into this specific overfitting geometry?”
Deep Learning #16
Apr 6, 2026
Senior ML Engineer interview at Google DeepMind
“You are training a dense recommender system. One feature dimension has violently massive gradient swings, while another dimension is completely sparse and barely updates at all. How do you stabilize it?”
Deep Learning #13
Apr 3, 2026
Senior ML Engineer interview at Google DeepMind
“During debugging, you notice your biases are updating rapidly, but your weight matrices are completely frozen, despite both sharing the exact same upstream gradient vector from the next layer. Looking at the isolated backprop equations for weight gradients versus bias gradients, what specific forward-pass state is mathematically guaranteeing this failure?”
Deep Learning #11
Apr 1, 2026
Senior ML Engineer interview at Google DeepMind
“You’re training a massive 50-billion parameter MLP on a cluster of H100 GPUs. Your monitoring tool shows the gradient norm has hit absolute zero, but your loss is still unacceptably high. What just happened?”
Deep Learning #9
Mar 30, 2026
Senior Machine Learning Engineer interview at Google DeepMind
“Your team is migrating a deep model’s hidden layers from Sigmoid to ReLU. Why are we doing this?”
Deep Learning #7
Mar 28, 2026
Senior AI Engineer interview at Google DeepMind
“You’ve implemented self-consistency (majority voting) to improve an agent’s math reasoning. But scaling the sample size from 10 to 40 yields zero performance gain. What is silently killing your scaling laws?”
LLM Agents #25
Mar 21, 2026
Senior AI Engineer interview at Google DeepMind
“Your theorem-proving LLM is blind to a researcher’s custom project. You stuff the entire active file into the 128k prompt context. Local accuracy spikes, but it still fails on cross-file theorems. Why?”
LLM Agents #21
Mar 17, 2026
Senior AI Engineer interview at Google DeepMind
“Your RLHF pipeline relies on top-tier medical and legal experts to score outputs. But as the model scales, your PPO updates start degrading its reasoning accuracy rather than refining it. What is breaking down, and how do you fix it?”
LLM Agents #20
Mar 16, 2026
Senior AI Engineer interview at Google DeepMind
“Your monolithic coding agent is handling both repo-wide search and patch generation, but as the context window fills up, the patch quality tanks. How do you architect the agent loop to fix this degradation?”
LLM Agents #19
Mar 15, 2026
Senior AI Engineer interview at Google DeepMind
“Your reasoning model hits 80% on miniF2F math benchmarks using just the current proof state. You deploy it to help researchers formalize a real paper in Lean, and its accuracy flatlines to 0%. Why?”
LLM Agents #18
Mar 14, 2026
Senior AI Engineer interview at Google DeepMind
“You upgraded your geometry autoformalization pipeline from a 70B text-only LLM to a state-of-the-art VLM. You feed it textbook diagrams alongside the text. Success rates barely nudge past 20%. Why?”
LLM Agents #16
Mar 10, 2026
AI Engineer interview at Google DeepMind
“Your new autonomous coding agent is hitting 40% on SWE-Bench. The PRs pass all historical unit tests perfectly. But when you manually review the code, the patches are technically incorrect and introduce massive regressions. Why is your agent passing the test but failing the engineering task?”
LLM Agents #15
Mar 9, 2026
Senior AI Engineer interview at Google DeepMind
“You’re building an autonomous coding agent to navigate a massive monorepo and generate bug patches. But by the time the agent actually finds the right files, its patch generation quality completely tanks. How do you architect the agent loop to fix this degradation?”
LLM Agents #12
Mar 6, 2026
Senior AI Engineer interview at Google DeepMind
“We need to fine-tune the VLM on paired image-to-equation datasets.”
LLM Agents #6
Feb 27, 2026
Senior AI Engineer interview at Google DeepMind
“Your zero-shot LLM keeps failing on Olympiad-level formal proofs because the reasoning depth is too vast. You have 10,000 A100 hours of inference budget to burn. Instead of naively sampling full proofs 100 times, what multi-stage decomposition strategy do you implement?”
LLM Agents #5
Feb 26, 2026
Senior AI Robotics Engineer interview at Google DeepMind
“We swapped our Actor-Critic stack for pure Q-learning to simplify our architecture. In our 14-DoF continuous action space, why does the standard argmax(Q) operation completely shatter our 5ms inference latency budget, and how do you fix it?”
Reinforcement Learning #24
Feb 19, 2026
Senior AI Research Engineer interview at Google DeepMind
“We are training a new RL agent to manipulate a robot arm for a task like pouring water. A junior engineer suggests initializing with standard epsilon-greedy exploration to discover the first high-reward state. Why is this mathematically doomed, and what is the production-ready alternative?”
Reinforcement Learning #15
Feb 10, 2026
Senior RL Research Scientist interview at Google DeepMind
“We’re training an agent for a sparse-reward, long-horizon task. Your Epsilon-Greedy agent is flatlining and stuck in local optima. However, a Thompson Sampling agent solves it efficiently. Why? What is the fundamental difference in how they treat uncertainty?”
Reinforcement Learning #14
Feb 9, 2026
Senior Research Scientist interview at Google DeepMind
“We’re training an end-to-end Meta-RL agent to find objects in a procedurally generated house. The loss curves are completely flat, the agent isn’t learning to explore or solve the task. Why is the gradient dead, and what is the fundamental coupling failure happening here?”
Reinforcement Learning #13
Feb 8, 2026
Senior RL Engineer interview at Google DeepMind
“We have 50TB of static historical logs. If we run a standard off-policy algorithm (like Soft Actor-Critic) on this buffer without collecting new data, what happens to the Q-values?”
Reinforcement Learning #12
Feb 7, 2026
Senior RL Engineer interview at Google DeepMind
“You’re implementing Conservative Q-Learning (CQL). To penalize out-of-distribution actions, you need to find the actions with the highest Q-values. Should we spin up a separate optimizer network to hunt for these maximums?”
Reinforcement Learning #10
Feb 5, 2026
Research Scientist interview at Google DeepMind
“I have a dataset of reasoning traces, but they’re all flawed. - 𝘛𝘳𝘢𝘤𝘦 𝘈 𝘴𝘵𝘢𝘳𝘵𝘴 𝘸𝘪𝘵𝘩 𝘱𝘦𝘳𝘧𝘦𝘤𝘵 𝘭𝘰𝘨𝘪𝘤 𝘣𝘶𝘵 𝘩𝘢𝘭𝘭𝘶𝘤𝘪𝘯𝘢𝘵𝘦𝘴 𝘵𝘩𝘦 𝘧𝘪𝘯𝘢𝘭 𝘴𝘵𝘦𝘱 (𝘍𝘢𝘪𝘭). - 𝘛𝘳𝘢𝘤𝘦 𝘉 𝘴𝘵𝘢𝘳𝘵𝘴 𝘸𝘪𝘵𝘩 𝘢 𝘮𝘪𝘴𝘵𝘢𝘬𝘦 𝘣𝘶𝘵 𝘭𝘶𝘤𝘬𝘪𝘭𝘺 𝘳𝘦𝘤𝘰𝘷𝘦𝘳𝘴 𝘵𝘰 𝘨𝘦𝘵 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘢𝘯𝘴𝘸𝘦𝘳 (𝘚𝘶𝘤𝘤𝘦𝘴𝘴). Standard Imitation Learning (SFT) will ignore Trace A and clone Trace B, including its mistake. How do we train a model that outperforms both?”
Reinforcement Learning #5
Jan 31, 2026
Senior AI Engineer interview at Google DeepMind
“Our competitor just trained a VLM on 6 billion image-text pairs. We only have the compute budget for 700k images. How do we beat them?”
Computer Vision #24
Jan 25, 2026
Senior AI Engineer interview at Google DeepMind
“We have a 70B parameter LLM. We need it to ‘see’ images. But here’s the constraint: We have zero budget to fine-tune the 70B weights, and we can’t afford to destroy the model’s existing reasoning capabilities.”
Computer Vision #23
Jan 24, 2026
Senior Computer Vision Engineer interview at Google DeepMind
“We need to classify 10,000 distinct car models (Make, Model, Year) for a demographics study. How do you build the model?”
Computer Vision #19
Jan 20, 2026
Senior Computer Vision Engineer interview at Google DeepMind
“Our YOLO model has 99% mAP ( Mean Average Precision ) on 𝘗𝘦𝘰𝘱𝘭𝘦 and 𝘍𝘪𝘳𝘦 𝘏𝘺𝘥𝘳𝘢𝘯𝘵𝘴 individually. But in production, we saw a person sitting on a fire hydrant, and the model didn’t flag it as anomalous. It just saw two boxes. Why did we fail, and how do you fix it?”
Computer Vision #18
Jan 19, 2026
Senior Computer Vision Engineer interview at Google DeepMind
“We use heavy data augmentation (Color Jitter, 30° Rotations) during training to improve robustness. Why do we strictly disable these during validation? Doesn’t that break the rule that 𝘛𝘳𝘢𝘪𝘯 𝘢𝘯𝘥 𝘛𝘦𝘴𝘵 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯𝘴 𝘴𝘩𝘰𝘶𝘭𝘥 𝘮𝘢𝘵𝘤𝘩?”
Computer Vision #13
Jan 14, 2026
Senior AI Engineer interview at Google DeepMind
“We just scaled our infrastructure to 4x our batch size (256 to 1024) to speed up training. We followed the 𝘓𝘪𝘯𝘦𝘢𝘳 𝘚𝘤𝘢𝘭𝘪𝘯𝘨 𝘙𝘶𝘭𝘦 and multiplied our 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘙𝘢𝘵𝘦 by 4. But our test accuracy still degraded. What fundamental property of SGD did we accidentally kill?”
Computer Vision #12
Jan 13, 2026
Senior MLE Engineer interview at Google DeepMind
“Our new foundation model is overfitting severely on the training set. Should we cut the hidden dimension size from 4096 to 1024 to limit its capacity?”
Computer Vision #6
Jan 7, 2026
Senior NLP Engineer interview at Google DeepMind
“We need to improve our 𝘑𝘢𝘱𝘢𝘯𝘦𝘴𝘦-𝘵𝘰-𝘌𝘯𝘨𝘭𝘪𝘴𝘩 translation model. We have 10k parallel pairs and 1 billion lines of monolingual English text. To use 𝐁𝐚𝐜𝐤-𝐓𝐫𝐚𝐧𝐬𝐥𝐚𝐭𝐢𝐨𝐧 effectively, which direction do we generate data, and exactly how do we pair it for training?”
NLP #25
Dec 30, 2025
Senior AI Engineer interview at Google DeepMind
“We’re training a massive binary text classifier. A junior engineer suggests using Hinge Loss because it creates a 𝘮𝘢𝘹 𝘮𝘢𝘳𝘨𝘪𝘯 and stops updating once a sample is correct, theoretically improving training stability. Why do we still prefer 𝐒𝐢𝐠𝐦𝐨𝐢𝐝 + 𝐋𝐨𝐠 𝐋𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 in production, specifically regarding the gradient signal on 𝘤𝘰𝘳𝘳𝘦𝘤𝘵 examples?”
NLP #16
Dec 22, 2025
Senior Machine Learning Engineer interview at Google DeepMind
“You just switched a 7B parameter training run from SGD to Adam to speed up convergence. The model size is identical, but the cluster immediately crashes with a 𝘊𝘜𝘋𝘈 𝘖𝘶𝘵-𝘖𝘧-𝘔𝘦𝘮𝘰𝘳𝘺 (𝘖𝘖𝘔) error. Why?”
NLP #12
Dec 18, 2025
Senoir Machine Learning Engineer interview at Google DeepMind
“We are building a massive Mixture of Experts (MoE) model. To maximize training throughput on our H100 clusters, we want to route each token to only the single best expert (k=1). Is this a valid strategy?”
NLP #11
Dec 17, 2025
Senior NLP Engineer interview at Google DeepMind
“We need to adapt our English-centric LLM to support Arabic and Hebrew. How do you adjust the tokenizer?”
NLP #9
Dec 15, 2025
Senior ML Engineer interview at Google DeepMind
“You are implementing 𝐋𝐨𝐑𝐀 (𝐋𝐨𝐰-𝐑𝐚𝐧𝐤 𝐀𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧) from scratch. How do you initialize the down-projection matrix A and the up-projection matrix B?”
NLP #6
Dec 13, 2025
final round ML Engineer interview at Google DeepMind
“How do you prove your word embeddings aren’t biased before we ship?”
NLP #4
Dec 11, 2025
Senior ML Engineer interview at Google DeepMind
“We are training a 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳 from scratch using 𝘈𝘥𝘢𝘮. We set a constant Learning Rate of 1e-3. Predict the first 1000 steps.”
NLP #1
Dec 9, 2025
Senior ML Engineer interview at Google DeepMind
“We have a 1B parameter Transformer that is SOTA on 10 languages. We wants to add 90 more languages to the training mix. What happens to our English benchmarks?”
ML System Design #23
Dec 7, 2025
Senior Machine Learning Engineer interview at Google DeepMind
“How do you handle these missing values before we start training?”
ML System Design #17
Dec 3, 2025
Machine Learning Research Engineer interview at Google DeepMind
“We need an automated trigger for model retraining based on feature drift. How do you implement it?”
ML System Design #16
Dec 2, 2025
Senior ML System Engineer interview at Google DeepMind
“We need to detect fraud in real-time. Write a function to calculate the running Global Median of transaction values coming from the payment stream.”
ML System Design #6
Nov 26, 2025
Senior Machine Learning Engineer interview at Google DeepMind
“We have a 1:1000 class imbalance for fraud detection. We applied 𝘤𝘭𝘢𝘴𝘴_𝘸𝘦𝘪𝘨𝘩𝘵𝘴 to the 𝐂𝐫𝐨𝐬𝐬-𝐄𝐧𝐭𝐫𝐨𝐩𝐲 loss, but the model is still missing the hard edge cases. What do we do?”
ML System Design #3
Nov 25, 2025
Machine Learning Systems Engineer interview at Google DeepMind
“You’re asked to budget a training run. An intern engineer estimates the total FLOPs as 2 * num_params * num_tokens, arguing the backward pass is roughly symmetrical to the forward pass. Why is this cost estimate off by 300%, and what two distinct gradient calculations (totaling 4x, not 2x) are they failing to account for?”
LLM System Design #24
Nov 22, 2025
Senior AI Engineer interview at Google DeepMind
“We’ve implemented the original DeepSeek GRPO paper to train our new math chatbot. On uncertain queries, the Chain-of-Thought (CoT) is suddenly exploding to 10000 tokens. An engineer on the team says this is great, the model is just thinking harder and learning to backtrack. What’s your diagnosis?”
LLM System Design #21
Nov 17, 2025
AI Engineer interview at Google DeepMind
“A new engineer implements RoPE by adding a rotational embedding to the token embeddings at the bottom of the model. The training loss is flat. What fundamental misunderstanding do they have about how and where RoPE is actually applied?”
LLM System Design #16
Nov 14, 2025
Senior ML Engineer interview at Google DeepMind
“You have just launched a new Mixture of Experts (MoE) training run. After a few thousand steps, you check the logs and see the validation loss has flatlined. What is the 𝐦𝐨𝐬𝐭 𝐥𝐢𝐤𝐞𝐥𝐲 𝐜𝐚𝐮𝐬𝐞 specific to an MoE, and how do you fix it?”
LLM System Design #12
Nov 10, 2025
Senior ML Engineer interview at Google DeepMind
“Our new reasoning model is great, but it uses a 2000 token Chain of Thought even for simple questions like ‘What is 2+2?’. This is killing our inference budget. How do you fix this without sacrificing its ability to solve complex problems?”
LLM System Design #10
Nov 8, 2025
AI Engineer interview at Google DeepMind
“Your 1B parameter proxy model trains perfectly with a 1.2e-4 learning rate. You scale the model to 70B, and the training immediately explodes. What’s the most 𝘭𝘪𝘬𝘦𝘭𝘺 reason and how do you fix it 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 running a new, expensive hyperparameter sweep?”
LLM System Design #7
Nov 5, 2025