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Anthropic interview notes, page 1 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

Apr 2026
Anthropic system design round, one big question about building a multithreaded image processing pipeline. Dense topic and they really wanted you to go broad AND deep, not just sketch a thread pool and call it done.
  • Design and implement a multithreaded image processing pipeline that handles a stream of images through sequential stages (decode, resize, filter, encode). How do you parallelize this for maximum throughput, and how do you handle thread pool sizing, work queues, producer/consumer synchronization, ordering guarantees, CPU-bound vs IO-bound stages, and backpressure or errors?

“This one sprawled fast.”

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Anthropic·Software Engineer·Technical Phone Screen

Apr 2026
Coding interview at Anthropic for a software engineering role, focused entirely on a tricky streaming data problem that combined two classic data structure questions into one. The design pressure was real and the follow-up complexity caught me a bit off guard.
  • Given a continuous stream of integers, design a data structure that supports two queries at any time: returning the current mode (most frequent value, with ties broken by smallest value) and returning the current median (lower middle element when count is even).

“Two problems duct-taped together, which I did not see coming.”

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

Apr 2026
Anthropic ML engineer interview focused entirely on production systems, not modeling. The whole thing was about what happens after you ship: infra, observability, performance tuning. Pretty intense if you've mostly lived in notebooks.
  • How would you scale the infrastructure for a production ML serving system, covering things like autoscaling, GPU vs CPU placement, multi-region deployment, and capacity planning?
  • What metrics and SLOs would you define for an ML production system, and how would you set up alerting and drift detection?
  • Walk through your approach to optimizing ML inference performance, including batching strategies, caching, quantization or distillation tradeoffs, and request shedding under load.
  • Walk through how you'd diagnose a p99 latency spike or an accuracy regression in a live ML system, end to end.

“I spent too long on the model side before realizing they explicitly didn't care about that.”

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Anthropic·Software Engineer·Technical Phone Screen

Apr 2026
Coding round at Anthropic for a software engineering role. Just one question: implement an LRU cache. Short session, but it's the kind of thing that trips you up if you haven't touched it recently.
  • Implement an LRU (Least Recently Used) cache with get and put operations in O(1) time.

“Classic question but I still fumbled the doubly linked list part for a minute.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Anthropic for a SWE role. Just one question but it hit harder than expected.
  • Describe a situation where you were fully confident in your solution, only to discover later that you were wrong.

“I had a story ready but partway through telling it I realized my example wasn't actually that good.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Anthropic for a software engineer role. Pretty focused on how you operate under constraints and whether you actually use AI tools in your day-to-day, which makes sense given who's asking.
  • How do you keep a project moving forward when you're working with limited resources?
  • How do you give feedback on a product when you're an engineer, not a PM?

“I pulled from a real situation where I had to cut scope and reprioritize without much guidance from above.”

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Anthropic·Software Engineer·Online Assessment (OA)

Apr 2026
Took the Anthropic OA for a software engineer role. It was a banking system problem, pretty close to something I'd seen before but with a couple of differences that ended up mattering more than I expected.
  • Implement a banking system with account management features, including scheduled payments and a getBalance() method that handles account merging and recreation correctly.

“The scheduled payment part was actually easier than a similar version I'd practiced before, so that was fine.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Culture round at Anthropic for a software engineering role. Pretty introspective set of questions, less about what you've built and more about whether you actually buy into the mission.
  • What is something about working at Anthropic that might make you want to leave?
  • Why do you want to join Anthropic specifically?
  • What is your view on where AI safety is headed and how does Anthropic fit into that future?
  • How would you describe the research culture at Anthropic, and does it appeal to you?
  • In what ways do you think Anthropic could be doing better?

“This one tripped me up a bit.”

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Anthropic·Product Manager·Hiring Manager Screen

Apr 2026
Interviewed for a PM role at Anthropic and got asked a pretty foundational AI question that I wasn't sure how to frame for a product context.
  • How would you define hallucinations in large language models?

“I knew what hallucinations were but fumbled explaining it in a way that felt PM-relevant rather than just textbook.”

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Anthropic·Software Engineer·Technical Phone Screen

Apr 2026
Anthropic software engineer interview focused on a task management system design problem. The core challenge was implementing filtering and listing with real discussion about data structure tradeoffs, not just code correctness.
  • Design and implement a task management system that supports listing tasks filtered by status, assignee, and priority in any combination, with sorting by fields like created_time or priority.
  • Walk through your data structure choices for this system. When would a linear scan with predicates be preferable over maintaining secondary indexes per field?

“The initial implementation felt straightforward, HashMap keyed by id for O(1) lookup, then just iterate and apply predicates for filtering.”

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