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Anthropic interview notes, page 17 of 32

Mid-sized

Most of these Anthropic write-ups come from Software Engineers (277), then Machine Learning Engineers (31) and Product Managers (10). The most reported rounds are the technical phone screen (129), the system design round (86), the behavioral / leadership round (45) and the hiring manager screen (17).

319 interview notes · updated Sep 2026

Anthropic·Software Engineer·Onsite - Coding / Algorithms

May 2026
Coding round at Anthropic for a software engineer role. One question, but you get a choice between two problems and have to implement plus walk through complexity. Pretty standard format but the follow-up discussion on the file dedup version can go deep fast.
  • Design an LRU Cache supporting O(1) get and put operations, with eviction of the least-recently-used entry when capacity is exceeded. Implement it and analyze the time complexity.
  • Given a list of directory path strings each containing filenames and their contents, group all file paths that share identical content. Implement a solution and discuss follow-ups around very large files, hash collisions, and a distributed setting.

“Classic hashmap plus doubly linked list setup.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Anthropic for a software engineering role. The whole thing was one big question about designing an async inference API, and it went pretty deep into queuing, scaling, and observability. Dense but fair.
  • Design an async inference service where clients submit jobs via POST and poll for results. Cover the request/response schemas, idempotency, job statuses, timeouts, retries, and rate limiting.
  • How would you design the job queue, worker pool, and storage for intermediate and final results for this inference service?
  • How would you scale workers efficiently, handle batching of inputs, and make use of hardware accelerators for inference?
  • How would you implement observability, error handling, and partial failure recovery for batch jobs in this service?

“I started with the polling contract because that felt like the core of it.”

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Anthropic·Software Engineer·Technical Phone Screen

May 2026
Anthropic software engineer interview with a debugging-focused coding round. The problem was a broken tokenizer implementation and you had to find the bug before fixing it.
  • You're given a tokenizer implementation that produces incorrect outputs on certain inputs. Read through the code, test it against some inputs, find the first bug, explain the root cause (which line, which case, why it fails), and then propose a fix.

“This tripped me up more than I expected.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Culture round at Anthropic for a software engineer role. The technical side felt more predictable but this round was harder to get a read on, the interviewer's face gave away nothing the whole time.
  • Why do you want to work at Anthropic specifically?
  • What is your perspective on AI safety and why does it matter to you?
  • Describe a moral or ethical dilemma you faced and how you handled it.

“They pushed back on anything that sounded too generic.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

May 2026
Anthropic system design round, one big open-ended question about GPU kernel profiling methodology. No coding, just a long whiteboard-style conversation about how you'd actually run experiments when all you have is a simulator spitting out cycle counts.
  • You have a kernel simulator that outputs cycle counts, IPC, stall reasons, occupancy, and memory bandwidth. Walk through a rigorous profiling and experimentation plan: how do you set a baseline, control variables, collect data, reduce variance, decide when to stop, attribute speedup to specific changes, and verify correctness after each iteration.

“This question is deceptively wide.”

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Anthropic·Software Engineer·Technical Phone Screen

May 2026
Coding screen at Anthropic for a software engineer role. The problem was a tokenizer implementation, which sounds straightforward until you get into the follow-ups about optimization and collapsing UNKs.
  • Implement a tokenizer that splits an input string into token IDs using a vocabulary dictionary. At each position, match the longest substring found in the vocabulary. If nothing matches, emit the UNK token ID and move forward one character. Signature: def tokenize(text: str, vocab: dict) -> list
  • Optimize the tokenizer so that at each position you only scan up to the length of the longest word in the vocabulary, rather than scanning the entire remaining string.
  • Further optimize the tokenizer to collapse consecutive UNK tokens in the output into a single UNK token.

“The core logic wasn't too bad once I stopped overthinking it.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Anthropic for a software engineering role, centered almost entirely on one big open-ended question about your highest-impact project. Be ready to go deep because the follow-ups are where the real interview happens.
  • Walk me through the project where you had the most impact. Cover the problem it was solving, why it mattered, your specific role, the hardest challenges you faced (technical or organizational), the trade-offs you made, how you measured success, and what you took away from it.

“This sounds like a normal behavioral prompt but it really isn't.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Anthropic for a software engineering role, focused entirely on building an inference batching layer in front of GPU model servers. Pretty deep technically, went well over an hour just on this one problem.
  • Design a batching API that sits in front of GPU-backed model servers, exposes a synchronous prediction endpoint to clients, and aggregates concurrent requests into batches internally to improve GPU utilization.
  • How would you handle routing across multiple models or model versions, and enforce per-tenant request quotas?
  • How do you deal with backpressure and queue overflow, and is there any optimization you can do for requests with identical inputs?
  • What failure modes do you need to plan for, specifically partial batch failures, model server crashes, and replica scaling behavior?
  • What observability would you build into this system, and which metrics matter most?
  • How would you support hot-swapping models, running A/B tests between versions, and autoscaling based on traffic patterns?

“This is basically the whole interview, not a warmup.”

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Anthropic·Software Engineer·Technical Phone Screen

May 2026
Anthropic SWE interview with a meaty algorithmic problem that kept growing. The core question was manageable but the follow-ups pushed into system design territory pretty fast, which I wasn't fully ready for.
  • Given an unsorted array of integers and a number n, determine if there are n distinct integers that form a consecutive sequence. Return any such sequence if it exists, or an empty result otherwise.
  • How would you adapt this consecutive-run detection to work on a live data stream that supports both insertions and deletions, while still being able to answer queries about whether an n-length run currently exists?

“Started with sorting which is the obvious move, got it working, then they asked about duplicates and negatives.”

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Anthropic·Software Engineer·Hiring Manager Screen

May 2026
Hiring manager screen for a software engineer role at Anthropic. Pretty standard chat, two questions about past and challenging projects, nothing too wild.
  • Walk me through a past project you've worked on.
  • What's the most challenging project you've tackled?

“Picked something recent and talked through what I built and why.”

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