Product Engineer - RL Platforms & Workflows

Clera; Oregon; Hybrid; USD 100,000 - 225,000; Full time

Benefits offered by this job

Visa sponsorship available

Remote-friendly

Job summary

Clera is seeking a product engineer to join a product engineering team building a platform and marketplace for reinforcement learning environments. You will combine product judgment, interaction design, and full‑stack engineering to make complex workflows clearer for environment creators and AI research teams.

You will ship software end‑to‑end, identify user friction with partners, interview users, and own improvements from problem definition through launch.

Qualifications

• Shipping software end-to-end from user needs to launch.
• Technical degree and full-stack web app experience.
• Experience interviewing users and using qualitative data.
• Strong interaction design and product judgment for complex workflows.
• Background in developer tools, data-rich products, or marketplaces.
• Familiarity with AI agents, reinforcement learning, or related workflows.

Responsibilities

• Identify user friction with product, design, and go-to-market partners.
• Speak directly with users to understand challenges in creating, testing, and discovering RL environments.
• Own product improvements from problem definition through implementation, launch, and iteration.
• Build frontend and backend features for onboarding, environment submission, review, and discovery workflows.
• Design interfaces that help users understand agent runs, evaluation results, and reward signals.
• Use user feedback and product data to refine experiences and inform tradeoffs.

Job description

Clera is seeking a product engineer to join a product engineering team building a platform and marketplace for reinforcement learning environments. You will combine product judgment, interaction design, and full‑stack engineering to make complex workflows clearer for environment creators and AI research teams.

You will ship software end‑to‑end, identify user friction with partners, interview users, and own improvements from problem definition through launch.