Anthropic SWE Interview: Code Review Round Guide

Estimated read time: 7-9 minutes

Summary: The Anthropic Code Review round sits within the HM Screen stage and is one of the less-discussed parts of the process, but it's a meaningful signal for senior candidates. You're given real or realistic code and asked to review it: find bugs, assess performance, and suggest improvements. What separates strong candidates isn't how many issues they find. It's the order and reasoning with which they address them.

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TL;DR + FAQ (read this first)

At-a-glance takeaways

• A discussion-based round; you review and critique code, you don't implement from scratch
• Most relevant for mid-level and senior candidates; lighter or absent for interns and new grads
• You're evaluated on your prioritisation as much as your ability to spot issues
• Correctness and safety come before performance; performance comes before style, always
• This round often runs as part of the HM Screen, not as a standalone session

Quick FAQ

Is this a live coding round?
No. You're reviewing code that's been provided: reading, analysing, and discussing it. You won't be writing an implementation from scratch.

What kind of code will I be reviewing?
Realistic backend/systems code: services, data structures, concurrent implementations, utility functions. Not trivially broken toy examples but code that has subtle issues baked into otherwise plausible-looking implementations.

How long do I have?
It varies, but typically 15-25 minutes within the broader HM screen. Expect to be time-constrained.

What if I don't find all the issues?
Missing some issues is expected; the code is often intentionally complex. What matters more is the quality of the issues you do find and how you reason about them. Finding the race condition and articulating why it matters beats finding five style nitpicks.

Is this round the same as the debugging task in the OA?
Similar in spirit but different in execution. The OA debugging task requires you to fix the code. The code review round here is evaluative and conversational. You're narrating your analysis, not necessarily writing fixes.

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What the code review round is actually testing

1) Engineering maturity and prioritisation

Junior engineers spot style issues. Senior engineers spot correctness risks. The code review round is calibrated to distinguish between them.

The interviewer is watching whether you treat a naming inconsistency the same way you treat an unhandled exception, or whether you instinctively rank risks by their potential impact. The latter is a strong signal of engineering maturity.

2) Safety-first thinking

Anthropic's engineering culture places correctness and safety above speed and cleverness. In a code review context, this means: do you flag the missing bounds check before you comment on the variable name? Do you notice the path where an error is silently swallowed before you suggest a performance optimisation? The order of your observations reveals your values as an engineer.

3) Concurrency and systems reasoning

Code presented for review often contains concurrency issues: race conditions, incorrect lock scoping, shared state without synchronisation. These are frequently the most important bugs in the code and the hardest to spot. Candidates who find them demonstrate systems-level thinking that's directly relevant to Anthropic's engineering work.

4) Communication under scrutiny

You'll be narrating your review process out loud. The interviewer is listening for clarity of reasoning. Can you explain not just that something is wrong, but why it's wrong and what the actual impact would be? "This could cause a race condition because two threads can read and modify this counter without synchronisation, which means under concurrent load you'd get incorrect counts" is a strong answer. "This doesn't look thread-safe" is not.

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How the round runs

The interviewer will share a code snippet, typically 30-80 lines of realistic backend code. You'll be given a moment to read it, then asked to walk through your review.

The format is conversational: you narrate what you're seeing, the interviewer may ask follow-up questions or react to your observations. Think of it as a collaborative code review meeting, not a quiz. The interviewer is not grading you on a fixed answer key. They're having a professional conversation about code quality.

Expect follow-ups like:

• "What's the worst-case impact of that bug?"
• "How would you fix it?"
• "If you only had five minutes, what would you address first?"
• "What test cases would catch this?"

These follow-ups are not traps. They're opportunities to demonstrate depth. A candidate who can answer "what would you fix first?" with a clear, prioritised rationale is showing exactly the kind of engineering judgment Anthropic values.

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The review order that reads as "strong"

The single most important thing to internalise is the priority ordering for your review. Strong candidates follow this sequence naturally:

1) Correctness and safety
Bugs that would cause incorrect behaviour, data corruption, security vulnerabilities, or crashes. Unhandled errors. Missing bounds checks. Silent failures. Race conditions. These are the highest priority and should be addressed first regardless of how minor they appear in isolation.

2) Performance
Algorithmic complexity issues, unnecessary allocations, hot paths with avoidable overhead, queries inside loops. These matter, but only after correctness is ensured. Optimising a broken system makes it a fast broken system.

3) Clarity and maintainability
Naming, structure, comments, decomposition. These matter for long-term code health. But a rename is never more important than a race condition. Raise clarity issues at the end, and frame them in terms of their maintenance impact, not personal preference.

If you find yourself wanting to lead with style comments, stop and ask: "Is there anything in this code that could cause incorrect behaviour or a safety issue?" Start there every time.

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What you might be asked to review

Code review questions cluster around a consistent set of scenarios based on candidate reports:

A concurrent data structure with subtle race conditions
A cache, queue, or counter that appears to use locking but has a critical section that's wider or narrower than it should be. Common bugs: checking a condition, releasing a lock, then acting on the result, allowing another thread to modify state between the check and the action.

An error handling failure
A service that handles the happy path correctly but silently swallows exceptions, returns incorrect default values on error, or fails to propagate errors to callers. The bug isn't obvious because the code "works" in normal conditions.

An O(n squared) algorithm hiding in a utility function
A function that looks simple but performs a linear scan inside a loop that's itself linear, or makes repeated calls to an expensive operation that could be cached. The correctness is fine, but the performance at scale is not.

A resource management issue
File handles, connections, or locks that aren't properly released in all code paths, particularly in error cases. The bug only manifests under failure conditions, which is why it wasn't caught in testing.

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Common failure modes

Leading with style issues. Walking through a code review starting with "this variable name isn't descriptive" while there's a race condition three lines later is a strong negative signal. It suggests you don't have an instinct for what actually matters in production code.

Identifying bugs without explaining impact. "This could throw a NullPointerException" is a weaker answer than "this dereferences a potentially null value, which would crash the thread handling this request and could cause silent failures in your response pipeline." Impact reasoning shows you've thought about the code in a production context, not just as an abstract correctness exercise.

Not suggesting fixes. You don't need to rewrite the code, but for each significant issue you raise, you should be able to sketch the fix, or at minimum describe the shape of the fix. Pure critique without any constructive direction reads as incomplete.

Rushing to cover everything. A shallow pass that enumerates ten minor issues is less impressive than a deep analysis of two significant ones. Quality of reasoning beats quantity of observations.

Missing the concurrency bug. If there's a thread-safety issue in the code, find it. These bugs are often the most impactful in production and the most telling to spot. Practise reading code specifically for shared state and synchronisation gaps.

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How to prepare

Practise narrating reviews out loud. Reading code and thinking about it silently is not the same as explaining your reasoning to someone else in real time. Find code in your domain and do timed verbal reviews: read it, then narrate your observations in priority order, out loud, as if talking to a colleague. This is a skill that degrades without practice.

Build a mental checklist for common production bugs. Every review should start with a sweep for: unhandled errors, incorrect synchronisation, resource leaks, missing input validation, and obvious algorithmic complexity issues. Make this reflexive so you don't miss categories under time pressure.

Study concurrency bugs specifically. Race conditions, deadlocks, incorrect lock scoping, and non-atomic read-modify-write patterns are staple review targets. If you're not completely fluent in identifying these in your primary language, that's the highest-leverage thing to fix before this round.

Practise the priority ordering under pressure. Take a piece of code with five issues baked in (two correctness bugs, one performance issue, two style issues) and practise ordering your review correctly every time. The habit needs to be automatic by the time you're in the interview.

Know how to describe impact, not just presence. For every bug you find during practice, force yourself to complete the sentence: "The impact of this in production would be..." If you can't complete it, dig deeper before moving on.

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