Anthropic’s Safeguards team builds systems to detect and mitigate misuse of AI models. This role owns the infrastructure behind research, developing tooling, pipelines, and workflows to support researchers in training, evaluating, and deploying detection methods. The engineer will solve large‑scale systems problems, build correctness and scalability into the stack, and partner with researchers and engineers to deliver reliable, production‑grade jobs.
Machine Learning Infrastructure Engineer, Safeguards Research en Anthropic
Híbrido - San Francisco, CA; New York City, NY, United States
Más vacantes en AnthropicSalary
USD 350,000 - 500,000
Requirements
Skills
- 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
Responsibilities
- 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
Technologies
PythonDistributed systemsData pipelinesGPU programmingInference optimizationMachine learning frameworksTransformersLanguage modelingExperiment trackingCaching layersEvaluation harnessesProbesInterpretabilityClassifier development
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