2 months ago
Base Salary
$405k - $625k/yr
Responsibilities
- Design widely used APIs, frameworks, and abstractions with legible interfaces and principled defaults.
- Embed with research teams, build systems supporting their work, and transfer ownership to teams that will maintain them.
- Improve the reliability and structure of research codebases without slowing research progress.
- Prevent silent failures through type safety, invariants, targeted testing, and refactoring.
- Support the health of production RL systems through monitoring, regression detection, and triage tooling.
- Define engineering standards, review practices, and design patterns while mentoring researchers and engineers.
- Design RL environment abstractions, sandboxed agentic model-tool interfaces, dataset lifecycle systems, data-access layers, probes, and statically verifiable coding standards.
Requirements
- Deep expertise in Python, including static typing, safe asynchronous and concurrent programming patterns, and performant code.
- A track record of designing intuitive, safe APIs or frameworks adopted by other engineers or teams.
- Experience working productively in large, evolving, or research-oriented codebases that the candidate did not originally write.
- Ability to anticipate and structurally prevent failure modes, especially silent failures, through system design, type safety, and testing.
- Strong written and verbal communication skills and the ability to explain system designs to collaborators with varied engineering backgrounds.
- Ability to scope work from loosely defined problems and drive it to maintainable outcomes.
- Preferred qualifications include experience with ML research or reinforcement-learning infrastructure, agentic systems, LLM training pipelines, large-scale distributed systems, sandboxed or remote execution platforms, data processing, dataset lifecycle management, plugin systems, team embedding or consulting, code standards and lint rules, technical leadership, or open-source maintenance.
- A bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field is required.
Benefits
- Hybrid policy requiring staff to work from an office at least 25% of the time, with some roles requiring more office time.
- Visa sponsorship is available, with immigration-lawyer support.
- Competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Tech Stack
Categories
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.