Full Stack Engineer, ML Research Tools

You will design and build full-stack tools for creating, reviewing, submitting, monitoring, and accepting reinforcement-learning environments and tasks. You will own review and acceptance workflows, build authoring interfaces, and develop data-management experiences that make training data easy to understand and improve.

Responsibilities

• Create workflows for vendor task creation, iteration, submission, and acceptance into training
• Build review tools for rollouts, transcripts, grader outputs, and task-quality signals
• Develop environment-health, failure-search, versioning, and catalog experiences
• Establish a shared component kit
• Build self-service interfaces for creating and improving tasks with inline quality checks

Requirements

• Experience shipping full-stack products from user interface through storage or services
• Experience with TypeScript and React alongside Node, Python, or Go
• Experience building dense data-facing tools such as transcript viewers, diffing systems, review queues, observability products, or operational dashboards
• Experience building or maintaining a design system or component library
• Experience designing review, QA, moderation, fraud, or acceptance workflows
• Knowledge of data quality and willingness to inspect raw data
• Ability to collaborate with researchers and domain experts and take open-ended problems to reliable products