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Clera

Forward Deployed Research Engineer

San Francisco$150k–$250kfulltimemidAdded today

About this role

Join a small, high-impact engineering team to build and maintain infrastructure for AI evaluation and reinforcement learning. You'll own end-to-end deployments for frontier AI labs, diagnose complex technical problems under pressure, and transform firefighting into scalable tooling.

What you'll do

  • Diagnose and resolve ambiguous technical problems across deployments for AI labs and data vendors
  • Own deployment requests from initial triage through completion, working independently with minimal guidance
  • Build custom tools and data pipelines to address urgent customer and partner needs quickly
  • Clarify underspecified requirements by asking the right questions to understand true business needs
  • Convert repeated manual work into reusable, documented processes and tooling
  • Coordinate with research and go-to-market teams to maintain deployment momentum

What they're looking for

  • Python programming
  • Docker containerization
  • Linux system administration
  • Benchmarking and evaluation frameworks
  • Reinforcement learning concepts
  • Debugging across code, data, and infrastructure
  • Independent problem-solving in ambiguous situations
  • Cross-functional communication and customer interaction

Benefits

  • Visa sponsorship available
  • On-site role in San Francisco with potential Singapore option
  • Small, high-ownership team environment
  • Work on frontier AI infrastructure and cutting-edge research
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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

  • Walk us through a time you debugged a complex production issue with incomplete information—how did you approach it?
  • Describe your experience building evaluation frameworks or benchmarks. How did you ensure rubric reliability?
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