Research Engineer, Reward Models Platform

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. This role will build tools and infrastructure that enable researchers to develop, evaluate, and optimize reward signals for training models. You will partner directly with researchers on the Rewards team and across Fine-Tuning to automate high-friction parts of research workflows, reduce experiment latency, and improve robustness of reward signals. Interviews for this role are conducted in Python.

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 to 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 best practices and documentation for reward development workflows

Requirements

• Prior research experience (required)
• Strong Python skills (interviews conducted in Python)
• Experience with ML workflows and data pipelines, and building related infrastructure/tooling/platforms
• Comfortable working across the stack, from data pipelines to experiment tracking to user-facing tooling
• Ability to scope ambiguous problems, ship quickly, and balance robustness with rapid iteration in a research environment
• Care about the societal impacts of AI and Anthropic's mission

Strong candidates may also have experience with:

• ML research and fine-tuning workflows
• 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

Representative projects

• Infrastructure to test rubric designs against small models before scaling
• Automated systems to detect reward hacks and surface problematic behaviors during training
• Tooling for comparing grading methodologies and understanding their effects on model behavior
• Data quality flywheels to identify problematic transcripts and feed improvements back into the system
• Dashboards and monitoring systems for reward signal quality across training runs
• Streamlining dataset preparation workflows to reduce latency and operational overhead

Compensation & Benefits

• Annual base salary: $315,000 - $340,000 USD
• Total compensation package for full-time employees includes equity, benefits, and may include incentive compensation
• Anthropic offers competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office space for collaboration

Logistics

• Locations: Remote-Friendly (travel required); offices in San Francisco, CA; Seattle, WA; New York City, NY
• Education: Minimum of a Bachelor's degree in a related field or equivalent experience
• Location-based hybrid policy: staff are expected to be in one of Anthropic's offices at least 25% of the time (some roles may require more)
• Visa sponsorship: Anthropic does sponsor visas and retains immigration counsel; sponsorship may not be possible for every role/candidate but the company will make reasonable efforts if an offer is made

How we're different

Anthropic focuses on large-scale, collaborative AI research that values impact on steerable, trustworthy AI. The team emphasizes empirical research, cross-disciplinary collaboration, and frequent research discussions to pursue high-impact work.