Anthropic Machine Learning Engineer interview questions

31 Machine Learning Engineers have shared notes from their Anthropic interviews, with the questions they were asked in each round. That is 10% of the 319 Anthropic interview notes on Screna AI.

31 Machine Learning Engineer interview notes

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

Jul 2026
System design round at Anthropic for an MLE role. The core problem was building a distributed cluster status tracker from scratch, which sounds manageable until the follow-ups pile on and you realize they want a full production-grade answer.
  • Design a cluster status tracker with methods to record node status updates (possibly out-of-order or duplicated), retrieve the current status of a node, retrieve the status at a specific point in time, and return a summary of node counts by status at a given time. Use last-write-wins conflict resolution by timestamp and nodeId. Aim for O(log n) per operation and support up to 100k nodes and 10M updates per day.
  • How would you extend the tracker to support range queries, such as getting per-minute node status counts over the last K minutes, efficiently?
  • How would you make the update and read operations thread-safe under high concurrency? Discuss sharding and locking strategies.

“This one took me a while to structure.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Interviewed for an ML engineer role at Anthropic. One technical question about controlling model creativity, which sounds deceptively simple but actually has a lot of depth to it once you get into the weeds.
  • How do you control or adjust the creativity of generative AI models?

“Went straight to temperature and top-p sampling, which is the obvious answer, but then they pushed further and I realized I hadn't thought carefully about the tradeoffs between them.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Coding screen for an MLE role at Anthropic. The problem was deceptively implementation-heavy for what sounds like a simple trace-parsing task, and the follow-up added a filtering layer that I didn't fully think through in time.
  • Given a sequence of function-name samples from a profiler (one per tick), convert it into a list of events where each event captures a function name and its [start, end) time interval. Consecutive samples of the same function belong to one event; a new function starts a new event.
  • Follow-up: modify your solution so that only events where a function appears for at least k consecutive samples are emitted. Shorter runs should be silently discarded.

“Sounds trivial until you're actually writing it out.”

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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·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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Anthropic. The focus was squarely on AI safety and data privacy, which makes sense given the company, but I still felt underprepared for how deep they wanted to go.
  • How do you ensure user data remains safe and private when designing and building AI systems?

“I started talking about encryption and access controls and could tell pretty quickly that wasn't the angle they wanted.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an MLE role at Anthropic and got a file deduplication problem that started simple but turned into a pretty involved system design conversation about scaling strategies. The follow-up questions on streaming vs batch were where things got interesting.
  • Given a list of files, each with a path and contents, group together all files that share identical content so duplicates can be identified. Return every group that has two or more files.
  • How would you handle very large files in this deduplication approach, and what are the risks of hash collisions causing false positives?
  • How does your approach change if files are arriving as a continuous stream rather than a one-shot batch you process all at once?

“The base problem is straightforward enough, hash the contents and bucket by hash.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Anthropic ML interview, one meaty technical question on probability calibration that covered a surprising amount of ground for a single prompt. Felt like a design question dressed up as a theory question.
  • You have a binary classifier whose raw output scores aren't well-calibrated. Walk through how you'd fix that using Platt scaling and isotonic regression, explain the tradeoffs between the two, and describe how you'd actually measure whether your calibration is any good.

“I knew Platt scaling well enough, it's just fitting a logistic regression on top of your model's scores using a held-out set.”

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Anthropic·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Anthropic. Just one question to go off of here, but it was the kind of thing that sounds casual until you're actually sitting there trying to not sound like you just read a few tweet threads.
  • How do you keep up with new developments in machine learning?

“I rambled a bit.”

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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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