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Clera

Full-Stack Software Engineer, Reinforcement Learning

San FranciscofulltimemidAdded today

About this role

Build end-to-end product experiences for a reinforcement learning data platform serving AI research labs and partners. Own everything from frontend dashboards to backend infrastructure, enabling teams to create, evaluate, and iterate on RL training data and environments.

What you'll do

  • Build product-facing tools for environment browsing, trajectory inspection, task quality review, and model behavior analysis
  • Develop vendor-facing workflows for RL environment and training data creation, submission, testing, and iteration
  • Create dashboards and observability tools for environment quality, evaluation results, data collection progress, and pipeline health
  • Design backend services and APIs connecting task authoring, data collection, evaluation, QA, and RL training infrastructure
  • Partner with research, operations, and go-to-market teams to ship systems rapidly with ambiguous requirements
  • Own user-facing and internal products end-to-end from design through deployment

What they're looking for

  • Full-stack software engineering (3+ years production systems)
  • Python
  • React, TypeScript, or Next.js
  • Backend services, APIs, and database design
  • Cloud infrastructure, Docker, CI/CD pipelines, production debugging
  • Dashboard and monitoring tool development
  • Data inspection and review workflow interfaces
  • High agency and stakeholder communication

Benefits

  • Competitive compensation
  • Full medical, dental, and vision coverage
  • 401k
  • Commuter benefits and gym membership
  • PTO plus company-wide holiday break
  • Access to leading AI development tools
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Describe a full-stack product you shipped end-to-end—what were the biggest technical and product challenges?
  • Tell us about a time you had to translate ambiguous or evolving requirements into shipped features. How did you handle unclear specs?
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