Software Engineer, Research Tools
Cursor · San Francisco
About this role
## Software Engineer, Research Tools ### About the Role As a Software Engineer on the RL Data team, you’ll design and build the tools researchers and external contributors use to create, review, submit, and monitor the environments and tasks behind Cursor’s reinforcement-learning runs. This is a full-stack product-engineering role embedded in a research team. You’ll own the review and acceptance experience end to end—from rollout and transcript inspection, task-quality signals grading and reward-hacking analysis, to the workflows that move a submission into training. You’ll also build authoring interfaces that let researchers, vendors, and domain experts create and improve environments and tasks quickly and confidently. Your work will significantly shorten the loop from a task idea or data sources → candidate task → trusted training data. ### What You’ll Work On - Create fast, trustworthy workflows for vendors and the research team to interact effectively with each other - Vendor task creation and iteration - Vendor submissions - Task acceptance into training - Build review tools for inspecting and comparing rollouts, transcripts, grader outputs, and other signals of task quality - Develop environment-health, failure-search, versioning, and catalog experiences to make training data easy to understand, manage, and extend - Establish a shared component kit, then use it to build self-serve interfaces for creating and improving tasks with quality checks inline ### You May Be a Fit If - You’ve shipped full-stack products and owned systems from UI through storage or services, using technologies such as TypeScript and React alongside Node, Python, or Go - You’ve built dense, data-facing tools (e.g., transcript viewers, diffing systems, review queues, observability products, operational dashboards) and have strong opinions about how structured data should be rendered - You’ve built or maintained a design system/component library and can establish durable product and engineering conventions for a fast-moving team - You’ve designed review/QA/moderation/fraud/acceptance workflows where users had an incentive to get past the checks—and you know how to keep those systems honest - You care about data quality and are willing to inspect raw data (experience with evaluations, graders, reinforcement learning, or data-quality systems is helpful but not required) - You move quickly under ambiguity, collaborate closely with researchers and domain experts, and take open-ended problems from rough need to reliable product ### Applying If there appears to be a fit, we’ll schedule two or three short technical interviews focused on: - Frontend craft for dense data - System design for a review-and-acceptance workflow After that, we’ll invite you onsite to work on a small project using real rollouts, discuss ideas, and meet the team. *#LI-DNI*
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