Software Engineer to build and scale Reinforcement Learning Environments (RLE) platform, which are interactive systems for frontier AI models to learn real-world tasks. The role involves owning components end-to-end, designing backend systems and data pipelines, and improving system reliability and performance, supporting model training and evaluation.

Software Engineer on the Coding Pod will build data infrastructure and pipelines for frontier AI coding models, focusing on creating large-scale, high-quality benchmark datasets for evaluating model performance on coding tasks. This role involves owning end-to-end data pipelines, integrating with developer ecosystems, and working with evaluation systems and agentic coding tools.

Associate Software Engineer to build Reinforcement Learning Environments (RLE) platform, including supporting infrastructure, backend systems, frontend interfaces, and data pipelines for model training and evaluation. The role involves creating modular workflow domains and working with senior engineers to improve system reliability and performance.

Software Engineer to build and scale the Reinforcement Learning Environments (RLE) platform, which involves designing and implementing backend systems, data pipelines, and modular workflow domains to support frontier AI model training and evaluation. The role requires experience in backend/distributed systems, ML-adjacent infrastructure, and cloud technologies.

Senior Software Engineer to build and scale Reinforcement Learning Environments (RLE) platform, simulating real-world workflows for AI model training and evaluation. This role involves driving architecture for scalable systems and data generation pipelines, partnering with research and product teams, and ensuring system reliability and observability.

Manager, Strategic Projects leading a team focused on AI data and evaluation work. Responsibilities include managing SPLs, driving project delivery (data pipelines, labeling workflows), translating needs into plans, owning performance metrics, ensuring a good experience for fellows, and partnering with Product/Engineering on tooling. Success involves consistent delivery, improved operational metrics, and strong team leadership. Requires 5+ years in operations, 2+ years managing teams, and experience with complex projects, ideally in AI data operations or ML ops.