RL Engineer

About the Role Join a small, early-stage team building training data and evaluation infrastructure for advanced AI models. You will design reinforcement learning environments, datasets, and evaluation methods that help models improve across complex, real-world tasks. What You'll Do Design data slices and agent tasks that reveal meaningful model failure modes across areas such as finance, coding, and enterprise workflows. Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines. Model annotator behavior and run experiments to improve model capabilities. Develop quantitative methods for measuring dataset quality, diversity, and impact on model alignment and capabilities. Create and manage real-world and synthetic data pipelines. Work with research teams to translate training objectives into concrete data and evaluation specifications. What We're Looking For One to eight years of experience as a machine learning or software engineer. Experience building reinforcement learning environments, or strong research experience developing agent tasks. Strong backend or full-stack engineering skills in Python, TypeScript, or similar languages. A computer science degree from a highly regarded institution, or an exceptional record of relevant experience. Experience measuring dataset quality and diversity, with AI research, publications, or relevant side projects as valuable signals. A bias for action, strong execution, and willingness to take on challenging, detailed work in a fast-moving team. Compensation & Benefits Salary range: $260,000 to $290,000 annually. Visa sponsorship is not available. Location Remote, with San Francisco, California as the primary location.