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Anthropic interview notes, page 24 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

Jun 2026
Anthropic system design round, one big question about building a multithreaded web crawler from scratch. Went deep on concurrency and I felt okay about the implementation side but the discussion about when to use threads vs async vs processes is where I started fumbling a bit.
  • Design a multithreaded web crawler that starts from a seed URL, crawls all reachable pages under the same hostname without revisiting any URL, and uses a worker pool pulling from a shared queue. Cover thread-safe deduplication, queue design, graceful shutdown, rate limiting, and when you'd choose threads vs async vs processes.

“This is a meaty one and it looks straightforward until you actually have to talk through all the moving parts at once.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Anthropic SWE interview that went deep on concurrency and distributed systems. The main problem was straightforward enough but the follow-ups are where things got interesting and a little uncomfortable.
  • Build an image-processing pipeline using a thread pool and queue. The pipeline should apply a series of operations (resize, grayscale, watermark, format conversion) to a batch of images and write results to an output location.
  • When does multithreading actually help versus hurt? Walk through the tradeoffs between threads and processes, especially in Python.
  • How would you scale this pipeline across multiple machines? Think about work distribution, failure handling, deduplication, monitoring, and back-pressure.

“I went with the obvious producer-consumer setup, thread pool pulling work items off a queue, applying ops in sequence, writing output.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Anthropic ML interview, one meaty coding/theory question that took up basically the whole session. They wanted you to actually derive things, not just recite formulas, which I was not fully prepared for.
  • Implement backpropagation from scratch for a small feed-forward neural network, including the forward pass, chain-rule gradient derivations for each layer's weights and biases, and the parameter update step using SGD. Also discuss vectorization, numerical stability considerations like softmax with cross-entropy, and how the approach scales with network depth.

“This wrecked me a little.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Interviewed for a SWE role at Anthropic and got hit with a system design question about building a token-generation service that could handle 100k requests per second. Heavy on the infrastructure and scalability side, which I wasn't fully prepared for.
  • Design a scalable token-generation service for an LLM that can handle up to 100,000 requests per second.

“I started with a basic load balancer and stateless service layer, which felt right, but I fumbled when they pushed on how you'd handle token uniqueness at that throughput without a central bottleneck.”

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Anthropic·Software Engineer·Online Assessment (OA)

Jun 2026
Took the Anthropic SWE online assessment and came out feeling pretty good about it. The question pool is small enough that grinding the known problems beforehand basically guarantees you'll recognize whatever shows up. Finished well ahead of time with full marks.
  • Implement a Recipe Manager system with the required functionality.

“This one's in the known pool so I'd seen it before going in.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Anthropic for a software engineer role, focused entirely on building a RAG pipeline from scratch. One long question that kept branching into follow-ups. Pretty intense but fair if you know the space.
  • Walk through how you'd design a RAG system that grounds an LLM in an external knowledge corpus, covering ingestion, retrieval, generation, evaluation, and keeping the index up to date.
  • What are the tradeoffs between HNSW and IVF index types for a vector database, and how would you decide which to use?
  • How would you handle latency requirements in a RAG system, and what specific techniques would you use to hit a tight response time target?
  • How would you evaluate a RAG system? What metrics would you track and how would you measure hallucination?
  • Explain how query rewriting fits into a RAG retrieval pipeline and when you'd use it.

“This was the whole interview basically.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Anthropic had me implement a full attention module from scratch in PyTorch, then defend every design choice under the hood. Pretty deep technical session, more like a research engineering screen than a standard coding round.
  • Implement a custom attention module in PyTorch from scratch. Given Q, K, V tensors, compute attention scores, apply scaling and masking, then normalize and produce the final output. The code should pass tests for shape correctness, gradient flow, and numerical stability.
  • Why is scaling by 1/sqrt(d_k) used in attention, and when should you use softmax versus layer normalization?
  • What are the time and memory complexity of the attention mechanism, and what optimizations would you propose for very long sequences?

“This was the bulk of the session.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a software engineering role at Anthropic and got a pretty deep technical question about compiler behavior on VLIW-style backends. Not the kind of thing you can fake your way through with buzzwords.
  • A modern compiler is mis-scheduling or over-optimizing code for a VLIW-like backend. Walk through how you'd guide it using pragmas, intrinsics, restrict/const qualifiers, and compiler flags. When would you resort to manual loop unrolling, software pipelining, or inline assembly, and how would you measure the impact without introducing new bugs? Also cover the risks around undefined behavior, aliasing, and portability.

“This was a lot to unpack in one question.”

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Anthropic·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a software engineer role at Anthropic. Two questions stood out from the technical discussion, one about managing tech debt while shipping features, and a motivation question about why I wanted to work there specifically.
  • How do you balance paying down technical debt while still delivering on feature requirements?
  • Why do you want to work at Anthropic specifically?

“This one actually tripped me up more than I expected.”

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Anthropic·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Anthropic system design round that went pretty deep on caching internals. The WAL persistence angle was not something I'd prepped for and it showed.
  • Implement an LRU cache that works like Python's functools.lru_cache decorator, including key generation from arbitrary positional and keyword arguments.
  • Add a persistence layer to the LRU cache using Write-Ahead Logging so that the cache state can be recovered after a crash by replaying the log.
  • What are the durability versus latency trade-offs with WAL in this context, and how would you handle WAL compaction or snapshotting?

“I started with the obvious doubly-linked list plus hashmap setup and felt good about it.”

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