Anthropic Machine Learning Engineer Interview Questions

5 years of experience interviewing for an ML infrastructure role. Unfortunately, I wasn't able to pass the interview but the interview process was fairly standard. ## Recruiter Call The call was fairl

Hi everyone! I'd like to ask if anyone has experienced this type of interview from Anthropic: "A 55-minute coding challenge on prompting and engineering with LLMs in Colab" This is essentially a 55-mi

Has anyone received a similar assignment? It requires two hours to complete, involving debugging kernel/assembly/compiler code on a Python emulator for performance tuning.

I'm torn between two topics, unsure which to choose. One is LLM, which has a strong impact, but I'm worried about being stumped by technical experts in the interview, especially since I haven't had ti

## Round 1 - System Design ## Problem Design a system that manages and distributes machine learning models to a fleet of edge devices (e.g., mobile phones, IoT sensors). The system must: - Allow data scientists to upload new model versions - Roll out models to device segments (e.g., 10% canary -> 50% -> 100%) - Track which model version each device is running - Support rollback if error rate spikes ## Key Components to Cover - **Model registry**: versioning, metadata, storage (e.g., S3 + database) - **Device registry**: device -> current version mapping, last heartbeat - **Rollout controller**: segment targeting, gradual percentage rollout, auto-rollback triggers - **Update delivery**: push vs. pull model; delta updates for large models - **Monitoring**: per-version error rates, latency, adoption metrics ## Follow-ups 1. How do you handle devices that are offline for weeks and miss multiple version jumps? 2. What consistency guarantees does the device registry need? Is eventual consistency acceptable? 3. How do you sign and verify model artifacts to prevent tampering on-device? 4. If model files are 500 MB, how do you minimize bandwidth cost during rollout?

## Problem You are given historical telemetry from a distributed service: `(timestamp, qps, p50_latency_ms, p99_latency_ms, error_rate, cpu_util)`. Build a model to predict `p99_latency_ms` and `error_rate` given a future `qps` and `cpu_util`. Walk through: 1. **Feature engineering** — what features to derive from raw telemetry 2. **Model selection** — linear regression, gradient boosting, or neural network; justify your choice 3. **Evaluation** — what metrics matter for ops use cases (MAPE, RMSE, quantile loss?) 4. **Serving** — how the model is used in a capacity planning workflow ## Example Scenario ``` Historical data shows: qps=500 -> p99=20ms, error_rate=0.1% qps=800 -> p99=45ms, error_rate=0.5% qps=1000 -> p99=200ms, error_rate=5% # near saturation Question: predict p99 and error_rate at qps=900 given cpu_util=75% ``` ## Follow-ups 1. How do you handle concept drift when the underlying system changes (e.g., new hardware)? 2. The latency vs. QPS relationship is nonlinear near saturation — how does your model capture that? 3. How would you quantify uncertainty in your predictions for risk-aware capacity planning?