Machine Learning Infrastructure Engineer, Safeguards Research

About the role

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

What you’ll do

• 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

What they’re looking for

Select a requirement to inspect fit, evidence or application context.

• 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
• A field relevant to the role as demonstrated through coursework, training, or professional experience

Helpful, not always essential

• 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
• The annual compensation range for this role is listed below.
• For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
• Annual Salary:
• $350,000—$500,000 USD