What are the top AI engineer interview questions at OpenAI, Anthropic, and Databricks with sample answers?
Updated June 9, 2026 · 9 min read · Crack ML Interview
OpenAI focuses on inference batching systems, rate limiter implementation, and KV cache internals. Anthropic tests token-generation services at 100K RPS, parallelism tradeoffs, and prompt injection mitigation at scale. Databricks emphasizes distributed GPU job scheduling, LRU cache variants, and Delta Lake ACID guarantees applied to ML feature stores. All three companies expect candidates to reason about scale explicitly and defend architectural tradeoffs under follow-up questioning.
OpenAI Interview Questions with Sample Answers
Design an inference batching system for a large language model serving API
Strong answers describe a continuous batching architecture using a system like vLLM, where new requests join in-flight batches as soon as a slot opens rather than waiting for a fixed batch to complete. Discuss how request queuing, priority classes for streaming versus batch clients, and KV cache memory management interact. Explain the GPU utilization tradeoff: larger batches improve throughput but increase time-to-first-token for individual requests. Mention autoscaling triggers based on queue depth and GPU memory saturation, and add a load balancer with affinity routing to keep KV caches warm.
Implement a rate limiter for an LLM API used by third-party developers
A complete answer covers the rate limit dimensions: requests per minute, tokens per minute, and concurrent connections. Discuss token bucket versus sliding window approaches and why token bucket is preferred for burst allowance. For the implementation, use Redis with atomic Lua scripts to increment and check counters in a single round trip, avoiding race conditions. At scale, distribute rate limit state across Redis cluster shards keyed by API key. Explain grace modes: return 429 with Retry-After header, optionally degrade to a smaller model before hard rejection.
Explain KV cache and describe two concrete strategies to reduce its memory footprint
The KV cache stores key and value projection matrices for each token in the context across all layers, so memory scales as sequence length times number of layers times hidden dimension times two times bytes per element. At fp16 with a 70B model, a 4K context can consume tens of gigabytes per request. Strategies to reduce footprint include grouped-query attention, which shares key and value heads across query head groups reducing cache size proportionally; quantizing the cache to int8 or even int4 at a small accuracy cost; and prefix caching, which reuses KV cache for repeated system prompt prefixes across requests.
Anthropic Interview Questions with Sample Answers
Design a scalable token-generation service handling 100K requests per second
At 100K RPS, the design must separate concerns sharply. Use a global API gateway for authentication and rate limiting, a request router that directs traffic to regional model serving clusters, and a queue with backpressure between the router and model servers to absorb traffic spikes without cascading failures. Each regional cluster runs vLLM with continuous batching behind an internal load balancer. Discuss how to size GPU fleets given average token length distribution, and describe a semantic cache layer that serves repeated or near-duplicate requests from cache, reducing effective load on the model tier by fifteen to thirty percent.
Explain the tradeoffs between pipeline parallelism and tensor parallelism for large model inference
Tensor parallelism splits individual layers across devices, requiring all-reduce communication at each layer boundary. This keeps latency low because all devices progress together but creates high communication overhead proportional to layer count, so it must be constrained to devices connected by high-bandwidth NVLink. Pipeline parallelism splits the model by layers across devices, with micro-batches flowing through stages, reducing communication to stage boundaries but introducing pipeline bubbles that hurt GPU utilization. For inference latency optimization, tensor parallelism within a single node and data parallelism across nodes is typically the better choice. For very large models, 3D parallelism combining both is used.
How would you detect and mitigate prompt injection attacks at scale for a customer-facing LLM product?
Detection approaches include a classifier trained on known injection patterns that runs on every input before the main model, embedding-based similarity to a library of known attack vectors, and a secondary LLM judge that evaluates whether the input attempts to override system instructions. For mitigation, apply input sanitization to strip delimiter patterns used in known attacks, enforce a strict system prompt that cannot be referenced or modified by user content through structural separation, limit what tools and data the model can access so successful injection has limited blast radius, and implement output filtering that scans responses for policy violations before delivery. Log all inputs and outputs for forensic review.
Databricks Interview Questions with Sample Answers
Design a distributed job scheduler for GPU compute workloads
The scheduler must handle heterogeneous job types: single-node training, multi-node distributed training, and inference serving. Core components are a job queue with priority classes, a resource manager tracking GPU availability and topology across the cluster, a placement engine that co-locates distributed training jobs on NVLink-connected GPUs to minimize inter-node communication, and a preemption policy for high-priority jobs. Add gang scheduling to prevent distributed jobs from starting until all required GPUs are available simultaneously, avoiding deadlock. Expose a REST API for job submission and a web dashboard for utilization visibility.
Implement an LRU cache with O(1) get and O(1) put, then extend it to an LFU variant
The standard LRU uses a doubly linked list for O(1) move-to-front and a hash map from key to list node for O(1) lookup. For LFU, maintain a hash map from frequency to a doubly linked list of keys at that frequency, a hash map from key to value and frequency, and track the current minimum frequency. On get, increment the key's frequency and move it to the next frequency bucket. On put when at capacity, evict the least recently used item from the minimum frequency bucket. Both operations remain O(1). Common follow-up: how would you make this distributed and consistent across multiple cache replicas?
Explain Delta Lake ACID guarantees and how they apply to an ML feature store
Delta Lake achieves ACID by writing data as immutable Parquet files and maintaining a transaction log of all commits. Atomicity: a write either fully appears in the log or does not, with no partial states visible. Consistency: schema enforcement and constraints prevent invalid data from being committed. Isolation: optimistic concurrency control detects conflicts at commit time, and snapshot isolation allows reads to see a consistent past version without blocking writers. Durability: the transaction log is written to durable storage before the commit succeeds. For an ML feature store, these guarantees enable point-in-time correct feature retrieval for training without data leakage, reproducible training datasets from past snapshots, and safe concurrent feature computation without corrupting the feature table.
AI Engineer Interview Question Types by Company and Frequency
| Company | Question Category | Representative Question | Reported Frequency | Depth Expected |
|---|---|---|---|---|
| OpenAI | Inference serving design | Design an LLM batching system | Very High | Production-grade detail |
| OpenAI | ML coding implementation | Implement rate limiter, write attention | High | Clean runnable code |
| Anthropic | Scale and reliability | Token-generation service at 100K RPS | Very High | Explicit tradeoff articulation |
| Anthropic | Parallelism tradeoffs | Pipeline vs tensor parallelism | High | First-principles reasoning |
| Anthropic | Safety and robustness | Prompt injection detection and mitigation | Moderate | Defense-in-depth thinking |
| Databricks | Distributed infrastructure | GPU job scheduler design | High | Systems and scheduling depth |
| Databricks | Coding and data structures | LRU/LFU cache implementation | High | Clean O(1) implementation |
| Databricks | Data and ML integration | Delta Lake ACID and feature stores | Moderate–High | Product and technical depth |
Who this is for
Strong ML infrastructure engineer targeting OpenAI or Anthropic for the first time
Profile: Has built GPU training pipelines and is familiar with distributed systems, but has not previously interviewed at research-focused AI labs and is unsure what depth level they expect.
Pain points: Tends to give complete but surface-level answers, correctly naming components but not proactively discussing memory arithmetic, failure modes, or scale reasoning that these companies specifically probe.
Strategy: Practice answering each question twice: once to get the structure right, then again to add one layer of quantitative depth, such as cache memory arithmetic or throughput estimates. Review reported questions from Crack ML Interview's question bank filtered to OpenAI and Anthropic to calibrate the exact depth these companies expect.
Data engineer at a large enterprise moving into an ML platform role at Databricks
Profile: Expert in Spark, Delta Lake, and data pipeline design, comfortable with Databricks products, but has limited exposure to ML-specific infrastructure like feature stores and model serving.
Pain points: Can answer Delta Lake and job scheduling questions deeply but struggles to connect data engineering concepts to ML use cases like point-in-time correct feature retrieval and training reproducibility.
Strategy: Study ML feature store architecture explicitly, focusing on how ACID guarantees prevent data leakage in training and how snapshot isolation enables reproducible dataset creation. Practice translating existing data engineering expertise into ML-framed answers, since this translation is exactly what Databricks interviewers reward.
FAQ
Q: Are these questions still relevant given how rapidly AI companies evolve their interview processes?
A: The core question categories at OpenAI, Anthropic, and Databricks have been stable for multiple hiring cycles, even as specific questions rotate. Focus on the underlying concepts rather than memorizing specific question wordings, and verify against recent debrief reports from platforms like Crack ML Interview.
Q: How much mathematical depth do these companies expect in system design rounds?
A: All three expect order-of-magnitude quantitative reasoning, such as memory arithmetic for KV cache or throughput estimates for a given GPU count, but not formal derivations. The signal they look for is whether you can reason numerically to defend architectural choices.
Q: Do I need experience at a previous AI company to get an offer at OpenAI or Anthropic?
A: No, but you need to demonstrate equivalent depth through project work, open-source contributions, or deep preparation. Both companies have hired strong engineers from non-AI backgrounds who showed first-principles reasoning about LLM systems rather than relying on pedigree.
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