Machine Learning Infrastructure Engineer, Safeguards Research

Anthropic's Safeguards team builds the systems that detect and mitigate misuse of our AI models, from individual policy violations to sophisticated, coordinated attacks. A growing part of that work depends on lightweight detection methods trained on model internals, which let us identify harmful behavior cheaply and at scale.

This work feeds directly into Anthropic's Responsible Scaling Policy commitments.

We're looking for an engineer to own the infrastructure behind that research. This is the tooling our researchers rely on to run experiments, train detection methods, and select detections for launch. It sits between research and production: researchers depend on it for fast iteration, and our detection systems depend on it for reliable, correct results as our models continue to change.

Running machine learning workloads at our scale often requires solving novel systems problems. You'll identify those problems and build the abstractions, pipelines, and tooling that keep the research loop fast as requirements shift underneath you. Strong candidates will have a track record of solving large-scale systems and data problems and will be excited to grow deep machine learning expertise alongside it.

- Build and scale the infrastructure and data pipelines behind Safeguards machine learning research

- Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the time between an idea and a result

- Design tooling and interfaces, including libraries and command line tools, that researchers can use directly without needing to understand the systems underneath

- Build correctness and sanity checking into the stack, so results stay trustworthy as models and workloads evolve

- Take the highest-value research workflows from experiments to reliable, production-grade jobs

- Improve the throughput, cost, and reliability of large-scale inference and scoring workloads

- Partner closely with researchers and engineers across Safeguards to understand their workflows, anticipate how their needs will change, and design for that ahead of time

- Strong software engineering fundamentals and hands-on coding ability, with proficiency in Python

- Experience building and operating data-intensive or distributed systems in production

- Experience building tooling or infrastructure that other engineers or researchers use as a dependency

- Comfort working across the research-to-deployment pipeline, from exploratory experiments to production systems

- Ability to debug performance and correctness problems across an unfamiliar stack

- Strong written and verbal communication skills, and a collaborative approach to technical decisions

- Experience with high-performance, large-scale machine learning systems

- Familiarity with language modeling and transformers, including working with model internals

- Experience with machine learning framework internals, GPU or accelerator programming, or inference optimization

- Experience building experiment tracking, caching layers, or evaluation harnesses for research teams

- Experience with probes, interpretability, or classifier development

- Interest in the misuse risks of AI systems and a desire to work on mitigating them