About the job
You will deeply understand the research workflows of our Finetuning teams and automate the high-friction parts – turning days of manual experimentation into hours. You’ll build the tools and infrastructure that enable researchers across the organization to develop, evaluate, and optimize reward signals for training our models.
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
Contribute to the development of best practices and documentation for reward development workflows
Qualifications
Minimum
Have prior research experience
Are excited to work closely with researchers and translate ambiguous requirements into well-scoped engineering projects
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
Can balance building robust, maintainable systems with the need to move quickly in a research environment
Are results-oriented, with a bias towards flexibility and impact
Pick up slack, even if it goes outside your job description
Care about the societal impacts of your work and are motivated by Anthropic's mission to develop safe AI
Preferred
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
Large-scale data processing (e.g., Spark, Hive, or similar)
Kubernetes, distributed systems, or cloud infrastructure
Familiarity with reinforcement learning or fine-tuning workflows