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Anthropic interview notes, page 9 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·Technical Phone Screen

May 2026
Interviewed for a software engineering role at Anthropic and got a concurrency systems question that was more involved than I expected. Not a lot of hand-holding, they wanted real implementation details and a discussion of failure modes.
  • Design and implement a thread-safe bounded blocking queue that supports concurrent enqueue and dequeue operations using mutexes and condition variables. Also discuss the time complexity and any deadlock risks.

“I went straight to the implementation which was probably the wrong move.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Anthropic for a software engineering role, two prompts focused on failure and cross-functional conflict. Pretty standard stuff but the expectation to quantify everything made it feel more rigorous than most behavioral screens I've done.
  • Tell me about a project that failed or fell short of its goals. How did you still manage to create impact despite that outcome?
  • Describe a conflict you had while working across teams like Product, Design, or Legal. How did you figure out what was actually causing it and get to a resolution?

“This one is harder than it sounds because you have to pick a real failure, not a humble-brag disguised as one, and then somehow show you still did something useful.”

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Anthropic·Machine Learning Engineer·Onsite - System Design / Architecture

May 2026
Brutal system design round at Anthropic for an MLE role. The prompt was essentially a full distributed systems design spec crammed into one question, and they wanted numbers, not hand-waving.
  • Design a highly available, multi-region service capable of handling 50k peak QPS with p95 latency under 100ms. Cover API design, storage schema, caching, consistency model, data partitioning, failure handling, and canary rollout strategy.
  • Do a back-of-the-envelope capacity plan: estimate read/write ratios, data growth over 12 months, peak vs average load, instance sizing, and network egress costs.
  • Build a performance model to predict end-to-end latency under load. Decompose service time, apply queueing theory approximations, and identify the system bottleneck.
  • What concrete mitigations would you propose for the bottlenecks you identified? Think batching, async workflows, indexing, autoscaling, circuit breaking, etc. Also define SLOs and describe how you'd load-test to validate your model.

“This is basically four interview questions duct-taped together.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML engineer role at Anthropic. The questions were heavily focused on AI safety and alignment, which made sense given the company, but I underestimated how deep they'd push on the ethics and misuse angles.
  • Your team is deploying a conversational AI model that reasons over sensitive topics. During internal testing you find the model tends to give confidently wrong answers in high-risk situations. How do you investigate and address that?
  • What are some ways a model like this could be misused, and how does alignment work factor into the system's overall credibility?
  • What data ethics and privacy concerns would you consider when building or fine-tuning a model trained on sensitive content?

“This one took me a minute to get my footing.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Culture-fit round for a software engineer role at Anthropic. Three questions, all values-focused, and the whole thing felt less like a behavioral interview and more like a philosophy seminar where you're also being graded on self-awareness.
  • Why Anthropic? Pick a specific company value that resonates with you, explain why it matters personally, and give a concrete example of a time you actually lived that value.
  • Tell me about a time you upheld a value when it was genuinely uncomfortable or in conflict with something else you cared about.
  • What do you think Anthropic could genuinely improve, not about the product, but about its policies, values, or how it operates as an organization?

“This is not the question where you talk about how much you like Claude.”

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Anthropic·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a software engineering role at Anthropic. The whole session was basically one algorithmic problem that kept getting harder, starting from a simple array scan and ending up in segment tree territory. Pretty intense for what felt like a single question.
  • Given an array of comparable elements, write a function that returns the maximum length of any run of consecutive equal elements.
  • Follow-up: given the same array and an integer N, return true if any run of consecutive equal elements has length at least N. What are the time and space complexities?
  • Further follow-up: now support many online suffix queries. For each query index i, determine whether the suffix a[i..m-1] contains a run of at least length N. Describe your preprocessing, data structures, and the query and update complexities, and discuss trade-offs between approaches like suffix run-length arrays, segment trees, and sparse tables.

“This part was fine.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML engineer role at Anthropic and got a system design question that went deeper than I expected. The distributed systems angle threw me a bit since I was more prepared for ML-specific stuff.
  • Explain the MapReduce programming model and describe how you would optimize a MapReduce job for both parallel computation efficiency and network utilization. What techniques reduce network overhead and improve throughput at scale?

“I started with the basics, map phase, shuffle, reduce, which was fine, but the optimization part is where I got a bit scattered.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Anthropic focused entirely on scaling a web crawler with concurrency. Pretty deep dive, more than I expected for a single session.
  • How would you refactor a single-threaded web crawler to run concurrently using a bounded thread pool?
  • Design a thread-safe URL frontier and a thread-safe visited set to avoid fetching the same URL twice.
  • Walk through the worker lifecycle in the thread pool: how do workers acquire tasks, and what are the termination conditions?
  • How do you implement per-host rate limiting, handle timeouts, and add retry logic for transient failures in a concurrent crawler?
  • How would you support cancellation and graceful shutdown of the crawler?
  • Compare coarse-grained locks, fine-grained locks, lock-free data structures, and message-queue based designs for the crawler. What are the tradeoffs in contention, throughput, fairness, and memory?

“I started with the thread pool sizing question and kind of rambled about CPU vs IO bound work before getting to the actual design.”

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Anthropic·Software Engineer·Technical Phone Screen

May 2026
Anthropic software engineer interview with a graph traversal problem that looks straightforward until you actually read the domain-filtering requirements carefully. The BFS skeleton is easy enough but the root domain extraction tripped me up a bit.
  • Given a directed graph representing a web link structure and a seed URL, return all pages reachable from the seed that share the seed's root domain, discovered via BFS, with duplicates removed and results in visitation order.

“The BFS part was fine, I've done enough of those.”

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Anthropic·Software Engineer·Technical Phone Screen

May 2026
Anthropic SWE interview with a web crawler problem, starting single-threaded and then getting pushed into a multi-threaded follow-up. Pretty classic problem but the hostname and fragment-stripping constraints added enough wrinkles to keep me on my toes.
  • Implement a single-threaded web crawler that starts from a given URL, uses a provided HTML parser to fetch links, and returns all unique reachable URLs restricted to the same hostname. URLs must have their fragment stripped before deduplication and before being included in the result.
  • Now make your web crawler multi-threaded. Multiple worker threads should be able to call the HTML parser concurrently, and the same URL must never be crawled more than once across all threads.

“BFS with a visited set, pretty mechanical once you figure out the hostname extraction.”

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