Anthropic MLE Interview Questions (2026)

Questions reported from Anthropic machine learning engineer interviews. This bank tracks 28 questions asked at Anthropic (10 with free model answers). Practice them live with DeepOffer's AI voice interviewer.

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#40 Architect a Production Service for Autoregressive LLM Generation ★
#41 How Would You Train Logistic Regression Using Gradient Descent from Scratch? ★
#50 State what the Chinchilla scaling laws claim and how they revised earlier intuitions about scale.
#57 Trace a single forward pass of a decoder-only transformer tensor by tensor.
#73 Name the key components of a Transformer and explain why each one matters.
#74 Explain attention-free transformer architectures and their trade-offs.
#100 Explain Constitutional AI, what it buys over vanilla RLHF, and what it does not solve.
#164 Explain how scaling laws should influence safety evaluation of large models. ★
#166 Explain continuous (in-flight) batching and why it replaced static batching. ★
#180 Design a batched inference system in which 100 requests take the same time as one. ★
#181 Design the serving stack for a Claude-scale LLM API that maximizes GPU utilization without wrecking p99 latency. ★
#241 Walk through the prompt-injection attack surface and defenses for an agent that reads inbound email and can reply and search internal docs.
#243 Explain how to detect and measure hallucinations in a production RAG system. ★
#254 Investigate and mitigate a conversational AI model that gives overly confident but factually wrong answers in high-risk contexts.
#294 Implement a BPE tokenizer from scratch: train merge rules, then encode and decode without external libraries. ★
#314 Implement a token-bucket rate limiter, then make it distributed. ★
#315 Build a rate limiter whose requirements grow every ten minutes (per-tenant limits, burst allowances, sliding window) without the code collapsing.
#321 Write the Python to run an LLM call over 50,000 documents given ~100 concurrency and occasional 429s and timeouts.
#322 Write an async batch processor over an API with concurrency limits, retries with jitter, and error isolation. ★
#323 Build an in-memory database: SET/GET/DELETE first, then filtered scans, TTL with timestamps, then file compaction.
#370 Explain why you want to work at Anthropic, and where you disagree with the company.
#371 Walk through a project you owned end to end and its key technical decisions.
#372 Describe a technical misjudgment that delayed a project.
#373 Share your thoughts on AI safety and the risks of advanced AI systems.
#394 Explain how you would design a Claude API implementation for a customer in a regulated industry.
#418 Design an autonomous travel-booking agent: orchestrator, tool gateway, credential management, rollback, and risk-classified actions
#422 Design a distributed search system serving 1 billion documents at 1 million QPS
#423 Design the tool surface for a coding agent: which tools exist, what their schemas look like, and how results come back