AI & ML ENGINEERING
Discovery Loop AI & ML Engineer interview questions
Discovery Loop was founded in August 2026 by Jeff Dean, Sanjay Ghemawat, Quoc Le and Oriol Vinyals, who left Google to build systems that automate entire experimental loops, starting with machine learning research and engineering itself. The company is new enough that no interview loop has been publicly reported, so treat anything you read claiming to know the rounds with suspicion, including this page if it ever starts guessing. What is knowable is the bar: this founding team built MapReduce, Bigtable, Spanner and TensorFlow, and the stated work is running thousands of experiments in parallel over large-scale computational infrastructure. Prepare for distributed systems at real scale, ML infrastructure, and the evaluation problem of deciding automatically whether an experiment actually improved anything.
16 concepts to master4 core topicsrole: AI & ML Engineer
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Representative AI & ML Engineer questions for Discovery Loop's loop
Discovery Loop's loop draws from these tracks. Here are the highest-signal questions in each, ordered by what candidates rate most useful.
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Go deeper on the topics Discovery Loop's loop tests
The tracks that map to a Discovery Loop AI & ML Engineer loop, ordered easy to hard.
The concepts Discovery Loop's AI & ML Engineer loop assumes you know
The vocabulary and mental models behind Discovery Loop's questions, from our curriculum. Start with the foundations free; the deeper, interview-defining ideas are part of premium.
ML INFRASTRUCTURE & SERVING
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Quantization and Low PrecisionQuantization stores and computes model weights (and activations) in fewer bits, FP16/BF16, FP8, INT8, INT4, instead of FP32, cutting memory and speeding inference at some accuracy cost. It is the main lever to fit a large model on a given GPU and to serve it cheaply, and it underlies QLoRA fine-tuning and KV-cache compression. Applied-AI interviews probe it because 'how do you serve a 70B model affordably?' usually starts with quantization, and knowing the precision ladder and its trade-offs is essential.Foundational
GPU Memory and the Serving StackServing an LLM is mostly a memory problem: the GPU must hold the model weights plus a KV cache that grows with sequence length and batch size, and inference splits into a compute-bound prefill and a memory-bandwidth-bound decode. Knowing the memory math (weights plus KV cache), why decode is bandwidth-bound, and the levers (quantization, batching, paged attention) is the foundation of LLM serving. Applied-AI interviews probe it because 'will this model fit and how fast will it run?' is a constant production question.CoreSign in
Knowledge DistillationKnowledge distillation trains a small student model to imitate a larger teacher, using the teacher's soft probability distribution (or internal features) as a richer training signal than hard labels. A student trained this way typically beats an identical model trained from scratch on the same data, because the soft targets encode the teacher's learned similarity structure. Applied AI interviews probe it because it is the main lever for shrinking a capable model into something cheap to serve, and because reasoning distillation and the legal terms around teacher outputs are live issues in 2026.Advanced🔒 Premium
Disaggregated Prefill/Decode and Prefix CachingLLM inference has two phases with opposite hardware profiles: prefill is compute-bound (it processes the whole prompt in parallel) while decode is memory-bandwidth bound (one token at a time). Running both on the same GPU pool makes them fight, so long prefills stall ongoing decodes and you miss either the time-to-first-token or the time-per-output-token SLO. Disaggregation runs them on separate GPU pools and transfers the KV cache between them, and prefix caching reuses KV for shared prompt prefixes. Applied-AI interviews probe it because it is the current frontier of serving architecture and a real latency-SLO tradeoff.SYSTEM DESIGN FOR AI IN PRODUCTION
Foundational
The LLM GatewayAn LLM gateway is a single proxy layer between your application and one or more model providers. It centralizes the cross-cutting concerns every LLM app needs: routing and fallback across models/providers, caching, rate limiting, authentication, cost tracking, observability, and guardrails. It also prevents vendor lock-in by abstracting providers behind one interface. Applied-AI interviews probe it because it is the backbone of a production LLM platform and the place most operational controls live.Foundational
Latency Budgets and StreamingLLM latency is not one number: time-to-first-token (set by prefill and queueing) and inter-token latency (set by decode) feel very different to users. Streaming tokens as they generate hides total latency by showing progress immediately. Designing to a latency budget means allocating time across retrieval, model, and tools, measuring TTFT and tokens-per-second (not just end-to-end), and using streaming, caching, and routing to hit it. Applied-AI interviews probe it because perceived latency makes or breaks LLM UX.Foundational
GuardrailsGuardrails are the runtime safety layer wrapping an LLM: input checks (detect prompt injection, off-topic or disallowed requests, PII) before the model, and output checks (content safety, schema/format validation, grounding, PII/secret leakage) before the user. They are built from rules, classifiers, judge models, and validators, with a defined fail-safe action when one trips. Applied-AI interviews probe it because 'add guardrails' is hand-wavy, and the concrete input/output checks plus fail-safe behavior are what make a deployment safe.Foundational
Rate Limiting, Retries, and BackoffLLM systems depend on rate-limited, sometimes-failing providers, so resilient design is essential. Rate limiting (token bucket) protects your service and enforces per-tenant quotas; retries with exponential backoff and jitter handle transient failures without hammering a struggling dependency; circuit breakers stop sending requests to a failing service to let it recover. Applied-AI interviews probe it because LLM calls are slow, expensive, and flaky, and naive retry logic turns a blip into an outage.MLOPS & LIFECYCLE
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Drift DetectionModels decay because the world changes. Data drift is a shift in the input distribution (detectable without labels by comparing live features to a training reference with PSI or KS tests); concept drift is a change in the input-to-output relationship (usually needs labels, which often lag). The discipline is monitoring inputs and predictions as leading indicators, alerting on sustained shifts, and triggering retraining. Applied-AI interviews probe it because 'the model was great at launch and quietly got worse' is a top production failure.CoreSign in
Model Debugging MethodologyModel debugging is the systematic process of root-causing why a model underperforms: deciding whether the cause is the data, the features, the labels, model capacity, or the evaluation itself, rather than blindly tuning hyperparameters. The method leans on error analysis over slices and the train/val/test gap ladder to localize the failure before fixing it. Applied AI interviews probe it because most candidates jump to bigger models or more tuning when the real bug is a leaky feature, a noisy label set, or a broken eval.CoreSign in
Model Registry, Lineage, and PromotionA model registry is the versioned source of truth for trained models: each model has a version, lineage (the data, code, config, and run that produced it), and a stage (staging, production, archived). It enables reproducibility, safe promotion through gates, instant rollback, and audit. Lineage is what lets you reproduce a model and debug a regression by diffing against the last good version. Applied-AI interviews probe it because shipping models without versioning and lineage makes rollback and debugging guesswork.CoreSign in
Reproducible and Deterministic PipelinesA reproducible pipeline produces the same model and metrics from the same inputs, achieved by pinning seeds, dependencies, data versions, and code together. Determinism on GPU is a separate, harder problem because many CUDA kernels are nondeterministic by default. Interviews probe this because without it you cannot debug a regression, pass an audit, or trust an A/B result.FOUNDATIONS OF LLMS & GENAI
Foundational
From RNNs to Transformers: RNN, LSTM, Seq2SeqRecurrent networks process sequences one step at a time through a hidden state, which makes them principled but slow and bad at long-range dependencies because gradients vanish across many steps. LSTMs and GRUs add gates to carry information further, and seq2seq encoder-decoder models with attention removed the single-vector bottleneck, which is the idea transformers then took to its conclusion. Applied-AI interviews probe this because it explains why attention exists and why we abandoned recurrence for parallelism.Foundational
Classic NLP: Bag-of-Words, TF-IDF, and Word2VecBefore learned embeddings, text was turned into sparse high-dimensional vectors with bag-of-words and TF-IDF, which count words and weight them by how distinctive they are but ignore meaning and order. Word2Vec and GloVe replaced counts with dense vectors trained so that words in similar contexts land near each other, which captures semantic similarity. Applied-AI interviews probe this because sparse methods still win as cheap baselines and as the lexical half of hybrid retrieval, and because they explain what dense embeddings actually fixed.Foundational
TokenizationModels do not read characters or words; they read tokens, subword chunks produced by an algorithm like BPE that maps text to integer IDs. Tokenization decides how many tokens a piece of text costs (driving price, latency, and context usage), why models miscount letters or fumble rare words, and why non-English text is more expensive. Applied-AI interviews probe it because token accounting is the first thing that bites a production LLM bill.Advanced🔒 Premium
Policy Optimization: PPO and GRPOPPO and GRPO are the reinforcement-learning algorithms that optimize an LLM against a reward, the RL step in RLHF and in training reasoning models. PPO is the established workhorse, updating the policy in small, clipped steps to stay stable; GRPO (used by DeepSeek-R1) drops PPO's separate value network and instead normalizes rewards within a group of samples, which is simpler and cheaper for LLMs. Applied-AI interviews probe it because it explains how alignment and reasoning training actually run, and why RL on verifiable rewards scales.DISCOVERY LOOP INTERVIEW FAQ
What is Discovery Loop?▲
Discovery Loop is an AI company founded in August 2026 by Jeff Dean, Sanjay Ghemawat, Quoc Le and Oriol Vinyals after they left Google. Its stated mission is automating discovery to accelerate science and engineering, beginning by automating machine learning research and engineering, then extending the approach to other domains with measurable outcomes.
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