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Anthropic interview notes, page 19 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·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
Behavioral question from an Anthropic interview, focused on failure and what you did with it afterward. Pretty open-ended, which sounds easy until you realize they want the full arc, not just the postmortem.
  • Tell me about a project that failed. What went wrong, how did you respond, and what lasting impact did you manage to create from the experience?

“The tricky part isn't describing the failure, it's the third piece.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a SWE role at Anthropic, got asked something that felt more PM-adjacent than I expected for an engineering screen.
  • How would you define the right goals, KPIs, or success criteria for a project?

“Wasn't expecting this from an engineering interview.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineer role. The whole thing centered on one big question about distributing massive ML model files to thousands of GPU hosts, which sounds straightforward until you start pulling on the threads.
  • Design a model downloader service that distributes large ML model artifacts (tens to hundreds of gigabytes) from a central store to potentially thousands of GPU inference hosts reliably and efficiently.
  • How would you handle atomic model version swaps on disk to avoid partial writes or serving a half-written model during an upgrade or rollback?
  • How would you minimize network egress from the central artifact store when a thousand hosts simultaneously request the same model?
  • How would you ensure download integrity and authenticity of model artifacts?
  • How would the inference scheduler know a model is ready on a host before routing traffic to it, and how does that interact with the download service?
  • How would you handle garbage collection of stale model versions on host disk without disrupting active inference workloads?

“This one sprawled in every direction.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a software engineering role at Anthropic. Just one coding question I can recall, focused on sorting accounts by transaction volume. Short but made me think about how to structure the aggregation cleanly.
  • Given a list of transactions, sort accounts by their total amount of money flowing in or out.

“The core of it is just aggregating per account and then sorting, but I spent too long second-guessing whether 'total' meant net or absolute.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Anthropic. One technical question about domain adaptation for generative AI. Short and focused, not much else to report.
  • How would you approach adapting a generative AI model to work well in a specific domain?

“This is the kind of question that sounds broad but they clearly want you to get concrete fast.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineering role. The prompt was one of those sprawling open-ended ones where they basically hand you a blank canvas and expect you to fill in every layer of the stack, from SLOs down to cost trade-offs and disaster recovery.
  • Design a scalable, highly reliable system for a complex open-ended domain (like a content feed, ride matching, or file storage). Walk through functional and non-functional requirements, high-level architecture, core APIs and data models, partitioning and replication strategy, caching across tiers, load balancing and autoscaling, failure handling and disaster recovery, observability and security, capacity planning, and key risks with mitigations.

“This is basically a 'design everything' prompt and the scope is genuinely intimidating.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Anthropic and got hit with a meaty question about content safety in generative AI. The kind of question that sounds straightforward until you're actually in it.
  • What makes it difficult to prevent generative AI systems from producing harmful or unsafe outputs, and what approaches would you take to address those challenges?

“I went straight into RLHF and red-teaming, which felt right but also kind of obvious for Anthropic of all places.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Interviewed for a software engineering role at Anthropic and got a system design question focused on batch inference. Short post, not much detail to go on.
  • Design a system for running batch inference at scale.

“Not a lot I can add here since the original post was pretty sparse.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a software engineering role at Anthropic and got hit with an LRU cache design question. Pretty classic but the O(1) constraint and the follow-up on concurrency made it more involved than I expected.
  • Design and implement an LRU cache class with get and put operations, both running in O(1) average time. The cache should evict the least recently used entry when it exceeds capacity.

“I knew the hashmap plus doubly linked list approach going in, which helped.”

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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 was one meaty problem about building a multithreaded web crawler, and it went deeper than I expected into concurrency and shutdown behavior.
  • Design and implement a multithreaded web crawler. Given a starting URL and an interface that returns all URLs on a page, crawl everything under the same hostname in parallel without revisiting any URL. Walk through your synchronization strategy and how you'd shut the system down cleanly.

“I started with the obvious stuff, thread pool, a shared visited set, a work queue.”

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