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Anthropic interview notes, page 25 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 - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineer role, except it wasn't really a typical system design round. No whiteboard, no diagrams, just a Google Doc and a conversation that kept looping back on itself every time I thought I'd landed somewhere solid.
  • Design a 'Prompt Playground': a tool that lets users write, test, and iterate on prompts for large language models. Walk through the full product and system design.
  • How would you handle very large prompts in the playground, specifically around context window limits, diffing between versions, and avoiding full re-runs when only part of the prompt changes?
  • How would you design the backend to store prompts, run histories, evaluation results, and per-user data at scale?

“I kept defaulting to backend-first thinking and the interviewer had to nudge me back to the UX side multiple times.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Technical presentation round for an EM role at Anthropic. You pick a significant project you led, walk through the whole thing, and then brace yourself for follow-ups that can go pretty deep into the weeds.
  • Walk us through a significant technical project or system you led or contributed to, covering how you framed the problem, the architecture, key design decisions and trade-offs, your specific role, results, and what you took away from it.

“This is basically the whole round.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineer role, focused on extending a task management system with dependency tracking and cycle detection. The problem looked manageable at first but the edge cases in the status transition logic took more thought than I expected.
  • How would you extend a task management system to support task dependencies, where a task can only move to 'in_progress' or 'done' once all tasks it depends on are already 'done'? Walk through the data model, how you'd enforce the transition rules, and how you'd reject invalid state changes.
  • When a new dependency is added between two tasks, how do you detect whether it would introduce a cycle in the dependency graph? What algorithm would you use and where does it live in the system?

“I started with the data model which felt straightforward, just adding a depends_on list of task ids.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineer role, focused entirely on building an LLM request batching layer in front of a GPU fleet. Pretty deep dive, went through batch formation, concurrency correctness, and multi-GPU scaling all in one session.
  • Design the core batching entry point: a blocking call that accepts a list of inputs, groups concurrent requests into batches, and returns outputs in the same order. How do you structure the in-memory buffer and what triggers a batch to flush?
  • How do you make the batching system thread-safe when hundreds of threads are calling the entry point simultaneously? What shared state exists and what protects it?
  • Generalize the design from one GPU to G GPUs. Compare a coordinator-push model versus a worker-pull model and argue for one.
  • If input lengths vary a lot, how would you batch by total token budget instead of request count, and what changes in the buffer and flush logic?
  • One request in a batch causes the GPU call to run 10x slower than normal. How do you prevent that from repeatedly hurting other requests in future batches?
  • How would you add request priority (like paid vs free tier) without starving low-priority requests?
  • What metrics would you instrument on this system in production, and which would you set alerts on?

“This is the meat of the whole problem.”

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Anthropic·Software Engineer·Online Assessment (OA)

Jun 2026
Anthropic's CodeSignal OA for a software engineer role is a multi-level banking system problem where each level stacks new requirements on top of the last. It's the kind of thing that feels manageable until you're three levels deep juggling scheduled payments and partial transfers simultaneously.
  • Design and implement a banking system that supports account creation, deposits, withdrawals, and transfers, then extend it across multiple levels to add scheduled payments, cashback logic, and partial transfer completion when funds are insufficient.

“The base level feels like a warmup but don't let that fool you.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Anthropic SWE interview with a coding problem in a Google Colab environment. The question was about converting stack samples into a trace, with example inputs and outputs provided to work from.
  • Write a function that takes stack samples as input and converts them into a trace, given example inputs and expected outputs.

“The Colab setup was a bit unexpected, felt more like a take-home vibe than a live screen.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineering role. The whole thing centered on one problem but went pretty deep, covering both implementation details and scaling considerations. Left feeling like I did okay on the basics but could've been sharper on the distributed side.
  • Given a directory tree or a stream of files, how would you identify duplicate files (files with identical content)? Walk through a basic implementation and then discuss how you'd scale it.

“Started with the obvious: hash file contents, group by hash.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Anthropic software engineer loop had an extra system design round tacked on, focused specifically on model distribution. Not what I was expecting going in.
  • How would you design a system for distributing machine learning models at scale?

“This was a surprise extra round so my brain was already a bit fried.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Anthropic software engineer interview with a classic LRU cache problem. The constraints were strict about O(1) for both reads and writes, so you couldn't get away with anything naive. Pretty standard coding round but the edge cases they listed made it clear they'd push on correctness.
  • Implement a fixed-capacity LRU cache supporting GET and PUT operations, both in O(1) time. When the cache is full, evict the least recently used key before inserting a new one.

“The core idea clicked fast for me, hashmap plus doubly linked list, but I fumbled the eviction logic when capacity was 1.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Anthropic software engineer interview with a meaty systems question about image processing. The whole thing centered on design and parallelism tradeoffs, which I wasn't fully prepared to articulate cleanly under pressure.
  • Design an image processor that applies a pipeline of operations to an image. Build both a single-threaded sequential version and a multi-processor parallel version. Cover how you'd partition the work, merge results, handle synchronization, and explain the tradeoffs between the two approaches.

“This question is bigger than it looks.”

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