Full Stack Engineer, ML Research Tools

Your work will significantly shorten the loop from a task idea or data sources, to candidate task, to trusted training data.

What you’ll work on

Create fast, trustworthy workflows for vendors and research team to interact effectively with each other — vendor task creation and iteration, vendor submissions, and 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 that 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 user interface through storage or services, using technologies such as TypeScript and React alongside Node, Python, or Go.

You’ve built dense, data-facing tools such as transcript viewers, diffing systems, review queues, observability products, or operational dashboards—and you have strong opinions about how structured data should be rendered.

You’ve built or maintained a design system or component library and can establish durable product and engineering conventions for a fast-moving team.

You’ve designed review, QA, moderation, fraud, or 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 and 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