Anthropic interview notes, page 6 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

System design round at Anthropic for a software engineer role, focused entirely on building a 1:1 chat system. The interviewer pushed hard on the transport layer choices and Kafka internals specifically, which I wasn't fully ready for.

Design a 1:1 chat system scoped to single-device web users. No group chat. Walk through the full architecture.
If the recipient is offline when a message is sent, how do you ensure they receive it once they come back online?
How would you detect whether a user is currently online or offline?
Compare Kafka and Redis as the backing transport for messages and presence data. When would you choose one over the other?
Walk me through Kafka internals: topics, partitions, replication, ISR, consumer groups, and offsets.
How would you handle session persistence and correct ordering of message history?

“I started with the usual WebSocket setup and a message store, which felt fine at first.”

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Anthropic·Software Engineer·Hiring Manager Screen

Apr 2026

Interviewed for a SWE role at Anthropic. Not a lot of technical depth in what was shared, just one question about how seriously the company actually takes its stated mission.

What concrete evidence would you point to that shows Anthropic is genuinely committed to its safety mission, rather than just using it as marketing?

“I fumbled this a bit.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at Anthropic for a SWE role, just one question about process frustrations.

Is there a work process or decision-making strategy you've strongly disliked?

“Tricky to answer without sounding like you just complain about your job.”

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Anthropic·Software Engineer·Onsite - Coding / Algorithms

Apr 2026

Coding round at Anthropic for a software engineer role. Just the one question but it had some depth to it.

Design and implement a file deduplication system.

“More interesting than it sounds at first.”

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Anthropic·Software Engineer·Technical Phone Screen

Apr 2026

Anthropic SWE interview that centered on building an LRU cache, but with the twist of serialization support and constraints that kept shifting as you went. Not a bad experience, just more layered than I expected.

Implement an LRU cache that supports serialization, and be ready for the requirements to change as you go.

“Started with the standard doubly linked list plus hashmap setup, felt fine.”

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Anthropic·Software Engineer·Online Assessment (OA)

Apr 2026

Anthropic software engineer OA, the main problem was a worker management simulation that apparently shows up in a known hiring challenge. Simpler constraints than the version I'd seen before, which helped a lot. Ended up with a full score.

Implement a worker management system where promotions take effect the next time a worker enters the office, salary is only paid when a grant fully covers a work session, and overlapping grants do not need to be merged.

“The version I'd prepped for had messier edge cases, so seeing these simplified constraints was a relief.”

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Anthropic·Software Engineer·Hiring Manager Screen

Apr 2026

Behavioral screen for a SWE role at Anthropic, pretty standard career retrospective stuff. Two questions, both focused on past experience rather than anything technical.

Walk me through the kinds of experiences you've built up over your career.
Tell me about a positive experience from your career and one that didn't go well.

“Pretty open-ended and I kind of rambled.”

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Anthropic·Software Engineer·Technical Phone Screen

Apr 2026

Anthropic software engineer coding round. The core problem was fine but the follow-ups are where things got rough, and I came out feeling like I passed the easy part and stumbled on the parts that actually mattered.

Implement a file deduplication system using file size and content hashing.

“The base problem was pretty straightforward.”

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Anthropic·Software Engineer·Technical Phone Screen

May 2026

Anthropic software engineer interview that went deep on caching internals. The main problem was implementing an LRU cache from scratch, and then they kept pulling the thread with persistence, serialization trade-offs, and atomicity. More to unpack than I expected.

Implement an LRU cache in Python that works as a decorator or class, handles both positional and keyword arguments correctly, and maps semantically equivalent calls to the same cache entry regardless of keyword ordering or argument type.
Add persistence to the LRU cache so it can be snapshotted to disk and restored when the process restarts. What on-disk format would you use, how do you version it, and how do you write atomically?
Compare pickle versus a JSON-based serialization approach for persisting the cache. Walk through the trade-offs around security, forward/backward compatibility, and performance.

“The canonical key part tripped me up more than the data structure itself.”

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Anthropic·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026

Behavioral and leadership-heavy loop at Anthropic for an MLE role. The questions were less about code and more about whether you can actually run a team and think clearly about messy ML products. Felt more like a senior engineering manager interview than a typical MLE screen.

Walk me through how you manage conflict, both within your team and across teams. Give concrete examples of diagnosing what's going wrong and how you resolved it.
How do you handle low performers on your team? Describe your approach to coaching plans, the timeline you'd use, and when you'd escalate.
How do you approach ranking team members fairly during a calibration process?
How do you set strategy and drive execution for a product area that's ambiguous and ML-heavy? What operating cadence and review mechanisms do you put in place, and what metrics do you track?
A critical project is at risk of missing its deadline. How do you respond?
Run a retrospective on a large-scale ML infrastructure project you led. Cover the goals, key decisions you made, what went well and poorly, measurable outcomes, and what you'd do differently.
Why would you choose this company over a comparable, well-regarded alternative? What values or trade-offs are driving that preference?

“I had a decent story ready about a cross-team dependency dispute but I fumbled the diagnosis part.”

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