← Back to Directory

Anthropic interview notes, page 26 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·Take-home Assignment

Jun 2026
Got a coding and design exercise for a Software Engineer role at Anthropic, done in Python on Google Colab. Pretty broad scope, covering everything from requirements clarification to testing to reproducibility. More of a take-home style prompt than a typical leetcode grind.
  • You're given an open-ended coding and design exercise in Python to complete in Google Colab. Walk through your full approach: clarifying requirements, picking data structures, structuring the code into modules or notebooks, handling I/O and config, logging, error handling, writing and running unit tests, analyzing complexity, managing dependencies, and making everything reproducible and documented for reviewers.

“This is basically asking you to describe your entire engineering process in one shot, which sounds manageable until you realize how much ground it covers.”

View Post

Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Interviewed at Anthropic for a SWE role and got a system design question about building a file cache. Not much else to report, pretty focused session.
  • Design a file cache system.

“Started with the basics, eviction policies, LRU vs LFU, and then got into storage layers pretty quickly.”

View Post

Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Anthropic SWE interview threw a pretty gnarly in-memory banking design problem at me. The scope was way broader than I expected for a single session, covering everything from timestamped balance history to pending transfers and merge semantics.
  • Design an in-memory banking service that supports timestamped account creation, deposits, payments, transfers with a two-phase accept mechanism, account merges, and historical balance queries. You need to specify data structures, return values for invalid operations, time/space complexity per operation, and a testing plan covering the merge and activity edge cases.
  • For the top_activity query, how do you efficiently compute, at a given timestamp t, the k accounts with the highest activity where activity is the sum of absolute operation amounts occurring exactly at t?
  • What exact return values should each operation produce for invalid inputs, such as depositing to a non-existent account, paying with insufficient funds, or querying a merged account at a timestamp after the merge?
  • Walk through a unit testing plan that specifically covers the merge and activity edge cases in this banking service.

“This was a lot.”

View Post

Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Anthropic phone screen for a software engineer role, one coding problem on file deduplication with a couple of follow-ups on performance and distributed systems. Worth noting that the portal showed basically nothing beforehand, no problem prompt, no structured question list, which threw me off a bit.
  • Implement a solution to detect and handle duplicate files.
  • If your solution is running slowly in production, how would you go about diagnosing the bottleneck?
  • How would your approach change if this system needed to run in a distributed environment?

“The official prompt was way shorter than what I'd seen discussed online.”

View Post

Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineering role, focused entirely on building a concurrent web crawler from scratch. The question had a lot of sub-parts and went pretty deep into concurrency models, which I wasn't fully prepared for.
  • Design and implement a concurrent web crawler starting from seed URLs, with deduplication, configurable depth and domain scope, and robots.txt support.
  • How would you structure the URL frontier to preserve BFS ordering and per-host fairness?
  • How do you prevent duplicate fetches across concurrent workers, and why does the visited check need to happen at schedule time rather than after fetching?
  • Compare async I/O, multithreading, and multiprocessing for a network-bound crawler. Walk through how each handles the frontier, per-host rate limiting, deduplication, retries, back-pressure, and graceful shutdown.
  • How do you reliably detect that the crawl is finished and not just that the queue is temporarily empty?
  • How do you handle transient versus permanent HTTP errors, set timeouts, implement retry with backoff, and bound memory and concurrency under load?
  • How do you enforce per-host politeness and respect robots.txt crawl-delay without hammering a target site?
  • Analyze the data structures and synchronization primitives used in your design. How do you prevent deadlocks and starvation, and what are the time and space complexity trade-offs across the three concurrency models?

“This was the whole interview basically.”

View Post

Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Anthropic ML infrastructure interview, system design round focused entirely on building a production inference serving platform. Pretty deep dive, they wanted real tradeoffs not just a laundry list of components.
  • Design an ML inference API service that can host multiple trained models and serve predictions over HTTP or gRPC. Walk through request routing, batching strategy, autoscaling, GPU and CPU resource allocation, model versioning with canary and shadow rollouts, monitoring for latency and drift, and how you'd define and meet reliability SLOs.

“This was basically the entire interview in one question.”

View Post

Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineer role, focused entirely on building an online model inference serving system. The scope was pretty broad and covered everything from request routing to GPU memory management, so it felt more like a senior-level architecture conversation than a typical design question.
  • Design an online model inference serving system. Walk through functional and non-functional requirements, the full request path, inference worker internals, reliability mechanisms, and observability.

“This one sprawled in a way I didn't anticipate.”

View Post

Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineering role. The whole thing was a deep critique and redesign of an ML inference API doc, covering pretty much every distributed systems concern you can think of. Dense, but interesting if that's your thing.
  • You're reviewing another engineer's design doc for an ML inference API. Critique it and propose improvements covering distributed systems concerns: SLOs, throughput capacity, autoscaling, batching, GPU scheduling, model versioning, multi-tenant isolation, rate limiting, backpressure, retries, cold starts, caching, token streaming, traffic shaping, monitoring, safety filters, and cost controls.

“This was basically the entire interview compressed into one prompt.”

View Post

Anthropic·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Anthropic for a SWE role, just one question from what I can tell. Pretty standard stuff but it made me realize I hadn't actually rehearsed a solid example for this one.
  • Tell me about a time you delivered difficult feedback to someone.

“I had an example ready but it felt thin the moment I started telling it.”

View Post

Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Debugging-focused technical screen for a software engineer role at Anthropic. The question was a single scenario-based problem and the whole conversation basically lived or died on how deep you could go with it.
  • You're debugging a production issue and the captured stack trace is incomplete. Only the bottom few frames are visible and there's no context about what called into them. Walk through how you'd investigate.

“This one had more layers than I expected.”

View Post