Anthropic aims to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. This role specifically focuses on solving the problem of inefficient and manual workflows in reward signal development for training AI models, by building scalable platforms and tools to accelerate and automate these processes.
Requirements
- Have strong Python skills
- Have experience with ML workflows and data pipelines, and building related infrastructure/tooling/platforms
- Are comfortable working across the stack, ranging from data pipelines to experiment tracking to user-facing tooling
- Experience with ML research
- Building internal tooling and platforms for ML researchers
- Data quality assessment and pipeline optimization
- Experiment tracking, evaluation frameworks, or MLOps tooling
Responsibilities
- Design and build infrastructure that enables researchers to rapidly iterate on reward signals, including tools for rubric development, human feedback data analysis, and reward robustness evaluation
- Develop systems for automated quality assessment of rewards, including detection of reward hacks and other pathologies
- Create tooling that allows researchers to easily compare different reward methodologies (preference models, rubrics, programmatic rewards) and understand their effects
- Build pipelines and workflows that reduce toil in reward development, from dataset preparation to evaluation to deployment
- Implement monitoring and observability systems to track reward signal quality and surface issues during training runs
- Collaborate with researchers to translate science requirements into platform capabilities
- Optimize existing systems for performance, reliability, and ease of use
Other
- We require at least a Bachelor's degree in a related field or equivalent experience
- Currently, we expect all staff to be in one of our offices at least 25% of the time
- Visa sponsorship is available, though not guaranteed for every role and candidate
- We encourage you to apply even if you do not believe you meet every single qualification
- We value communication skills and collaboration, and host frequent research discussions to ensure high-impact work
