Machine Learning Infrastructure Engineer, Safeguards Research
$350,000–$500,000 year
HybridNew York City, New York, United States or San Francisco, California, United States
Job Summary
Build and scale infrastructure and data pipelines for Safeguards machine learning research, owning training, evaluation, and scoring workflows to cut time from idea to result. Design libraries and command-line tools that researchers use directly, embedding correctness and sanity checks to ensure trustworthy results as models evolve. Take high-value research workflows from experiments to production-grade jobs while improving throughput, cost, and reliability of large-scale inference and scoring workloads. Partner with researchers across Safeguards to anticipate workflow changes and design ahead of time. This role supports Anthropic's Responsible Scaling Policy commitments by enabling lightweight detection methods trained on model internals.
Required Qualifications
- 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
- Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience
- Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
- Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time
Desired Qualifications
- 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
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