Senior Full-Stack Engineer for AI Training Tools

Clera; San Francisco (CA); On-site; USD 130,000 - 225,000; Full time

Job summary

Clera seeks a design engineer to build product interfaces for AI training environments, blending design judgment with front-end and full-stack engineering to create workflows that are clear, useful, and actionable for users.

You will collaborate with research and engineering to turn reinforcement learning workflows into usable tools, talk with users to identify pain points, and raise the bar for usability, visual quality, and implementation.

Qualifications

• 3+ years shipping product interfaces in design engineering or related roles.
• Experience turning designs into working front-end interfaces; full-stack experience is relevant.
• Portfolio of shipped design-engineering work with evidence of user-feedback-driven improvements.
• Strong technical fundamentals and ability to work with engineers and researchers.
• Ability to explain technical concepts clearly.

Responsibilities

• Design and build product interfaces from early concepts through launch.
• Make agent runs, evaluation results, reward signals, and quality feedback understandable.
• Partner with research and engineering to turn reinforcement learning and evaluation workflows into useful tools.
• Talk with users to observe struggles and improve the product based on their feedback.
• Develop reusable interaction patterns and maintain high usability, visual quality, and implementation.

Skills

Front-end engineering

Design engineering

Product design

Full-stack engineering

User feedback analysis

Education

Technical education or strong technical fundamentals

Tools

Developer tools

Reinforcement learning

Research software

Job description

Clera seeks a design engineer to build product interfaces for AI training environments, blending design judgment with front-end and full-stack engineering to create workflows that are clear, useful, and actionable for users.

You will collaborate with research and engineering to turn reinforcement learning workflows into usable tools, talk with users to identify pain points, and raise the bar for usability, visual quality, and implementation.