Are these based on real engineering roles?
Yes. The catalog is built around real engineering role patterns so the practice round feels closer to a live interview.
Company practice
Anthropic mock interviews
Pick a role, answer follow-up questions out loud, and get a scored verdict after the interview.
4 rolesPythonC++CUDADistributed Systems
Create custom interviewRoles to practice
Anthropic
ML / AI
Performance Engineer, Inference Systems
This role optimizes the systems that serve Claude to millions of users, squeezing maximum throughput and minimum latency from large GPU clusters running frontier transformers. Work spans kernel-level optimization, batching and scheduling, and end-to-end profiling of the inference path. A technical interview would probe GPU performance fundamentals, the mechanics of LLM serving (KV cache management, speculative decoding, continuous batching), and how to find and fix the bottleneck limiting tokens-per-second in a production serving stack.
CUDAC++TritonAnthropic
Security
Research Engineer / Scientist, Frontier Red Team (Cyber)
The Frontier Red Team evaluates the cyber and offensive-security capabilities of frontier models, building challenging environments and evaluations to measure what Claude can and cannot do in realistic attack scenarios. The role combines security domain expertise with ML experimentation to inform Anthropic's responsible scaling commitments. A technical interview would probe practical offensive-security knowledge (exploitation, CTF-style problem solving), how to design rigorous and non-gameable capability evaluations, and building RL or agentic environments that elicit and measure a model's security-relevant behavior.
PythonReinforcement LearningOffensive SecurityAnthropic
Research
Research Engineer, Interpretability
The Interpretability team reverse-engineers the internal computations of large language models — studying features, circuits, and superposition — to make Claude's behavior understandable and steerable. Research Engineers build tooling and run experiments that turn mechanistic hypotheses into measurable results on frontier models. A technical interview would probe transformer internals (attention, residual stream, MLP layers), designing experiments to isolate and validate a learned feature or circuit, and writing efficient code to extract and analyze activations at scale.
PythonPyTorchJAXAnthropic
Infrastructure
Staff+ Software Engineer, Infrastructure
Staff Infrastructure Engineers build and scale the clusters that train Claude and the production systems that serve it reliably to millions of users, solving novel scaling challenges few organizations face. They lead design of large training and serving infrastructure, balancing reliability, cost, and developer velocity. A technical interview would probe large-scale distributed systems design (fault tolerance, scheduling, storage), reasoning about failure modes in a multi-thousand-node GPU cluster, and tradeoffs in building platforms that let researchers iterate quickly without sacrificing production stability.
PythonKubernetesDistributed Systems
ExoForm is not affiliated with Anthropic. This is an independent practice page.
FAQ
Is the interview voice-based?
Yes. ExoForm runs a live voice interview, asks follow-ups, and produces structured feedback after the session.
Can I try it for free?
Yes. You can start with the free interview allowance before upgrading for more practice.