Job Details

We're building a gamified developer platform where tens of thousands of engineers create highfidelity datasets that push LLM frontiers. This role owns the technical lifecycle of data pipelines-from defining new data formats with partner labs to shipping the tooling, environments, docs, and QA that make those formats real at scale.

What You'll Do

• Own projects end-to-end, from initial prototyping to ongoing maintenance, bug fixing, and iteration based on feedback.
• Own developer experience pipelines endtoend: Prototype tooling for collecting new data formats → productionize workflow → iterate from developer experience
• Champion DX: Create clear, concise guidelines and documentation to empower our data contributors and ensure high-quality inputs for your projects.
• Quality & governance: Develop and manage the quality standards for your projects, which includes training and aligning content reviewers to ensure data consistency and accuracy. Implement automated checks, eval harnesses, reviewer workflows, and data quality bars; be hands on and in the weeds to align with reviewers on standards.
• Maintain & iterate: Monitor, debug, and continuously improve reliability, latency, and contributor success rates.

• Define Frontier data formats: Coauthor specs/RFCs with frontier lab researchers; design schemas, metadata, and versioning for new task/trajectory formats.
• Build developer tooling & environments: Ship tooling, sandboxes, CLIs/SDKs, and capture/instrumentation to make contribution flows fast and safe.

• Excellent written communication skills, with a proven ability to explain complex concepts to a less technical audience.
• An organized and process-oriented mindset - you enjoy bringing structure to ambiguous problems and are meticulous about quality.
• Foundational full-stack skills, with experience in React and at least one modern backend language (e.g., Python, Node.js, Go).
• Strong technical judgment and a pragmatic mindset - you know how to balance speed with quality, recognizing the need for a scrappy solution versus when to invest in a robust architecture.
• A deep resourcefulness with AI - you are highly adept at prompt engineering and using AI tools to find the fastest path to a solution.

• Curiosity, pride in your work, desire to push the frontiers

• Experience designing or running evaluations for LLM outputs to measure and track quality, accuracy, or other performance metrics.
• Familiarity with building tools for other developers, such as CLIs, SDKs, or internal dashboards.
• Experience with cloud infrastructure (AWS), Docker, and CI/CD pipelines