Clera Product Engineer to build an AI platform for creating and evaluating reinforcement learning environments. Combine product judgment, interaction design, and full-stack engineering to improve complex workflows.
Responsibilities
Join a product engineering team building an AI platform for creating and evaluating reinforcement learning environments. You will combine product judgment, interaction design, and full-stack engineering to make complex workflows more clear and useful for creators and research teams.
Work with product, design, engineering, research, and go-to-market partners to identify user friction and prioritize improvements.
Talk with environment creators and research teams about how they create, test, evaluate, and discover environments.
Define, prototype, build, launch, and iterate on improvements across the platform and marketplace.
Develop frontend and backend features for onboarding, environment submission, review, and discovery.
Design interfaces that help users understand agent runs, evaluation results, reward signals, and quality feedback.
Use user feedback and product data to evaluate outcomes and guide tradeoffs.
Qualification
Full-stack web application experienceA technical education or backgroundSkill designing clearBackground in developer toolsFamiliarity with AI agents
Required
Experience shipping software products end to end, from identifying user problems through implementation and iteration.
Full-stack web application experience, including polished frontend interfaces and backend services or APIs.
A technical education or background, with strong product judgment and the ability to make decisions with incomplete information.
Experience conducting user interviews and applying qualitative feedback and product data to improve products.
Skill designing clear, usable interaction patterns for complex technical workflows.
Background in developer tools, data-rich products, marketplaces, or products serving distinct user groups.
Familiarity with AI agents, evaluations, reinforcement learning, training data, or operational workflows such as review, debugging, and quality feedback.
