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Anthropic interview notes, page 29 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

Jul 2026
Onsite system design round at Anthropic for a software engineer role. The interviewer was unusually warm and actually typed notes into the shared doc while I talked so I could stay focused on thinking, which was a nice change from the usual awkward silence while you wait for someone to catch up.
  • Design a prompt playground (a tool that lets users write, test, and iterate on prompts for a language model).

“The interviewer kept typing as I talked, which sounds small but actually changed how I thought through the problem.”

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Anthropic·Software Engineer·Technical Phone Screen

Jul 2026
Coding round at Anthropic for a software engineer role, centered entirely on one problem with five escalating parts. The core concept was straightforward but the edge cases kept piling up in ways I didn't fully anticipate going in.
  • Implement an IP address iterator class that takes a starting IPv4 address and a step size as constructor arguments, then yields successive addresses by advancing that step each time. The class should include next() and hasNext() methods and correctly handle overflow across octets, wrap-around, very large steps, and both string and integer representations.

“The first part felt easy and I got comfortable fast, which was a mistake.”

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Anthropic·Machine Learning Engineer·Take-home Assignment

Jul 2026
Take-home style SQL and Python task for an ML engineer role at Anthropic. The whole thing was about working with a demo database and figuring out whether the data was actually usable.
  • Given access to a company demo database, extract and clean a usable dataset using only SQL and Python.
  • Assess whether the data meets regulatory formatting standards and whether it's ready for deep visualization or analysis tasks.

“This was more open-ended than I expected.”

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Anthropic·Software Engineer·Technical Phone Screen

Jul 2026
Anthropic software engineer interview that went deep into compiler-level stuff, specifically VLIW scheduling on a 4-issue machine. Not what I expected from a software role. The whole session was basically one extended technical problem with several follow-up layers.
  • Given a basic block represented as a dependency DAG, produce a legal instruction schedule for a 4-issue VLIW machine with two ALUs, one load/store unit, and one special-function unit. Latencies are 1 cycle for ALU ops, 3 for loads, and 2 for special-function ops. Maximize parallel issue and minimize stalls.
  • How did you handle hazards and register pressure when building your schedule?
  • If this basic block were inside a loop, how would you approach software pipelining to improve throughput?
  • How does your schedule change if average memory latency increases by one additional cycle?

“This was the core of the whole interview.”

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Anthropic·Software Engineer·Online Assessment (OA)

Jul 2026
Anthropic SWE interview, CodeSignal-style multi-level coding problem. Level 1 was a recipe service with two functions to implement. Felt manageable but the edge cases around case-insensitive matching and return values tripped me up more than I expected.
  • Implement a recipe service with two operations: one to register a new recipe owned by a user (with a name, timestamp, ingredients, and steps), and one to record a rating from a user for a named recipe. Handle duplicates and missing recipes with appropriate return values, and treat recipe names as case-insensitive.

“The core logic wasn't the hard part.”

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

Jul 2026
System design round at Anthropic for an MLE role. The whole thing was one long, sprawling question about distributed data processing and I spent most of it feeling like I was one follow-up away from losing the thread completely.
  • Design a large-scale data processing system using a MapReduce-style architecture. Cover input/output schemas, sharding strategy, and how you parallelize computation across workers.
  • How would you minimize network traffic in this system? Think about data locality, combiners, serialization format choices, compression, and request batching.
  • How do you handle data skew and straggler tasks in a distributed processing job?
  • Walk me through your fault tolerance and retry strategy, and how you'd decide between at-least-once and exactly-once delivery semantics.
  • Give a complexity analysis for your design and rough estimates for throughput and latency under realistic load.
  • What metrics would you track and what experiments would you run to validate that your system is actually efficient?

“I started with the schema stuff because it felt safe, but the interviewer kept pushing toward partitioning before I was ready.”

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Anthropic·Software Engineer·Onsite - Coding / Algorithms

Jul 2026
Coding round at Anthropic for a software engineer role. The main question was a file deduplication problem with a solid chunk of follow-up discussion that pushed into system design territory pretty quickly.
  • Given a list of file paths or a directory, implement a function to find duplicate files. You should first group files by size, then confirm duplicates within each group using a content hash like SHA-256.
  • Is file deduplication I/O-bound or CPU-bound, and how does that affect your choice between threads and processes?
  • How would you handle hashing very large files efficiently? What about short-circuiting the comparison early?
  • If you had to scan millions of files, what are the memory and performance concerns, and how would you address them?
  • How would you extend this system to support real-time or continuous duplicate detection as files are added or changed?

“The two-stage approach is pretty natural once you think about it: size check is basically free, so you filter down before doing any expensive I/O.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Anthropic for a software engineering role. The main problem was designing a 1-on-1 chat system, which went fine until the deep dive into Kafka internals completely fell apart.
  • Design a 1-on-1 chat system. Consider offline message delivery, user presence detection, and session storage.
  • Compare Kafka and Redis for this use case. What are the trade-offs and when would you pick one over the other?
  • Walk me through Kafka internals. How do partitions, consumer groups, and offset management actually work?

“Covered the main pieces well enough.”

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Anthropic·Software Engineer·Onsite - Coding / Algorithms

Jul 2026
Coding round at Anthropic for a software engineer role. Started with a single-threaded web crawler and then got pushed into a multi-threaded extension with a bunch of concurrency discussion. Pretty solid round if you're comfortable with systems-adjacent coding problems.
  • Implement a single-threaded web crawler starting from a seed URL, using BFS to follow links, deduplicating visited pages, and handling basic URL normalization.
  • Extend your crawler to run with multiple threads. How do you synchronize the visited set, design the work queue, size the thread pool, and handle the difference between I/O-bound and CPU-bound work?

“The BFS part came naturally but I spent probably too long bikeshedding over URL normalization edge cases before writing any actual code.”

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Anthropic·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026
Culture fit round for a software engineer role at Anthropic. The whole thing was essentially one long structured conversation about values, consistency, and where the company might be getting things wrong. More thoughtful than I expected but also kind of exhausting.
  • Which specific Anthropic value or operating principle resonates with you most, and why does it actually matter to you personally?
  • Tell me about a time you held to a value when it was uncomfortable, unpopular, or created friction with others.
  • How do you demonstrate that this value shows up consistently in your day-to-day work, not just in big moments?
  • Where do you think Anthropic could genuinely improve, specifically around trade-offs in policy, values, or how the org operates?

“I'd prepared something for this but it still felt a little awkward to say out loud.”

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