Machine Learning Engineer

Overview

Own the infrastructure that supports Safeguards research at Anthropic, including the training, evaluation, and scoring workflows used to detect and mitigate misuse of AI models.

What you'll do

• Build and scale infrastructure and data pipelines behind Safeguards ML research.
• Own training, evaluation, and scoring workflows used by researchers.
• Design tooling and interfaces such as libraries and command-line tools for researchers.
• Embed correctness and sanity checking into the stack.
• Move high-value research workflows from experiments to production-grade jobs.
• Improve throughput, cost, and reliability of large-scale inference and scoring workloads.
• Partner with researchers and engineers to anticipate changing needs across the research-to-deployment pipeline.

What you'll need

• 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 used by other engineers or researchers as a dependency.
• Ability to work across the research-to-deployment pipeline from experiments to production.
• Ability to debug performance and correctness issues across an unfamiliar stack.
• Strong written and verbal communication skills and a collaborative approach.

Details

• Location-based hybrid policy: expected to be in one office at least 25% of the time.
• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience.
• Visa sponsorship: company sponsors visas for some roles/candidates.