Remote Full-Stack Design Engineer for AI/RL Tools

Clera Labs, Inc.; San Francisco (CA); Hybrid; USD 130,000 - 225,000; Full time

Benefits offered by this job

Visa sponsorship available

On-site in SF with remote options

Competitive base salary

Job summary

Clera Labs, Inc. is building AI infrastructure for reinforcement learning environments.

You will design and build interfaces across the platform and marketplace to help users understand agent behavior, improve training data, and make informed decisions. You will collaborate with research and engineering to turn complex reinforcement learning and evaluation workflows into useful tools, talk with users, observe challenges, and iterate based on feedback.

Qualifications

• 3+ years shipping product interfaces with a full-stack engineering background.
• Experience turning design work into working product interfaces.
• Portfolio with shipped design-engineering work and visual quality.
• Experience shipping products, learning from users, iterating from feedback.
• Technical education or background; clear communication with engineers and researchers.
• Experience with developer tools, data-rich products, AI agents, RL, or early-stage company experience is useful.

Responsibilities

• Design and build interfaces across the platform and marketplace from concept to release.
• Make agent runs, evaluation results, reward signals, and feedback actionable.
• Collaborate with research and engineering on RL workflows and tooling.
• Talk with users, observe challenges, and improve the product from feedback.
• Develop reusable interaction patterns and maintain usability and visuals.

Skills

Front-end engineering

Full-stack background

Design-engineering

User feedback iteration

Education

Technical education background

Tools

Research software

AI agents

Job description

Clera Labs, Inc. is building AI infrastructure for reinforcement learning environments.

You will design and build interfaces across the platform and marketplace to help users understand agent behavior, improve training data, and make informed decisions. You will collaborate with research and engineering to turn complex reinforcement learning and evaluation workflows into useful tools, talk with users, observe challenges, and iterate based on feedback.