Responsibilities

• Design and Iterate Training Task Sets: Create curated task datasets that teach specific agent capabilities, such as code generation quality, debugging, or complex programming patterns. Analyze traces and evaluation metrics to identify what's working and iterate on task design based on model performance data, ensuring continuous improvement in agent effectiveness.
• Analyze Agent Traces and Identify Failure Modes: Conduct deep analysis of model behavior from training traces and inference logs to discover failure patterns, unexpected behaviors, or edge cases. Build systematic approaches to surface and reproduce these modes, converting ad-hoc findings into scalable datasets that help the model learn from mistakes.
• Develop Reward Functions and Evaluation Systems: Design reward functions that accurately measure task performance and align with Cursor's goals for agent effectiveness. Create robust evaluation systems that provide meaningful signals about model progress, enabling data-driven decisions about training effectiveness and dataset quality.
• Build Reusable Data Infrastructure: Transform one-off scripts and recipes into production-ready systems that other teams can leverage. Abstract reward logic, environment setup, and data quality checks into maintainable, well-documented components that scale across the organization and improve data team productivity.
• Collaborate with Research Teams: Partner with researchers to validate that datasets are actually teaching the intended capabilities, not just improving benchmark scores. Conduct analyses to understand model learning dynamics, propose data improvements, and ensure training datasets align with real-world deployment requirements.
• Own Data Quality and Validation: Establish and maintain high standards for training data quality, including validation pipelines, outlier detection, and data lineage tracking. Ensure datasets are clean, representative, and properly documented for reproducibility and future reference.
• Scale Real User Data Integration: Work on systems to incorporate real user data into RL training pipelines, ensuring privacy, quality, and relevance. Design scalable data collection and processing infrastructure that enables frontier coding agents to learn from actual user interactions and coding patterns.

Benefits

• Equity Compensation

  Significant stock option grants as an early-stage employee at Cursor, a well-funded AI startup working on frontier technology with potential for substantial upside.

• Health and Wellness

  Comprehensive health insurance coverage including medical, dental, and vision plans for employees and their families.

• Professional Development

  Access to learning resources, conference attendance budgets, and opportunities to work on cutting-edge AI research and machine learning systems.

• Flexible Work Arrangement

  Remote-friendly work environment with flexibility to work from San Francisco or distributed, enabling work-life balance while collaborating with a talent-dense team.

• Collaborative Culture

  Flat organizational structure with direct access to leadership, emphasis on spirited technical debate, and an environment that values creative problem-solving and shipping fast.

• Meaningful Impact

  Direct influence on frontier coding agent capabilities that shape the future of AI-assisted software development, working on problems at the frontier of reinforcement learning and code generation.