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AnthropicAnthropicSan Francisco, CA

Machine Learning Infrastructure Engineer, Safeguards Research

Build and own ML infrastructure, data pipelines, and tooling for Safeguards research at Anthropic. Focus on fast researcher iteration for training/evaluating lightweight detectors on model internals while ensuring correctness at scale. Requires strong Python, distributed systems, and production infrastructure experience.

350k – 500k/yr
Hybrid5+ YOEML Engineering

About the role

Key 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

Minimum 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

Preferred 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

Education

  • Bachelor’s degree or an equivalent combination of education, training, and/or experience in a field relevant to the role

Skills

PythonDistributed SystemsData Pipelinesmachine learning infrastructureTransformersgpu programminginference optimizationexperiment trackingevaluation harnessesprobesinterpretability

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