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Software Engineer, RL Environments

Cursor · San Francisco

onsiteunknownPosted Sep 16, 2026

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About this role

## Software Engineer — RL Environments ### About the role Help automate coding by building the systems that turn real-world data into high-quality reinforcement learning (RL) environments and tasks. You’ll create dramatically more realistic training environments while owning shared platforms and quality layers that enable trustworthy RL data at speed and scale. This role sits at the intersection of research, data, and engineering. You’ll work closely with ML Platform and research teams to convert company data, Grok Bot interactions, vendor-built tasks, tutor data, acquired data, and synthetic data into training-ready environments. ### What you’ll work on - Build platforms to create complex, realistic, and diverse RL environments at scale, supporting end-to-end tasks across knowledge-work domains. - Design an end-to-end “factory” that turns massive raw data into useful, realistic environments. - Define and apply consistent quality standards across vendor, tutor, acquired, and synthetic data. - Create self-serve APIs and tooling that accelerate task and environment development for internal teams and external contributors. - Partner across research, platform, and external teams to translate model-capability goals into effective training tasks and environments. ### You may be a fit if - You have strong software engineering fundamentals and experience with data platforms, developer tools, distributed systems, or ML infrastructure. - You can turn ambiguous quality standards into concrete, automated checks. - You’re comfortable building repeatable pipelines from messy, heterogeneous data. - You care about model behavior and can translate capability goals into tasks and experiments. - You collaborate well across disciplines and can own open-ended problems end to end. ### Applying If there appears to be a fit, the team will reach out to schedule **2–3 short technical interviews**. After that, you’ll be scheduled for an **onsite** in their office to work on a small project, discuss ideas, and meet the team.

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