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Clera

Forward Deployed Research Engineer

San Francisco$150k–$250kfulltimemidAdded today

About this role

A forward-deployed research engineer role focused on solving urgent, ambiguous technical challenges for frontier AI labs and data vendors. You'll diagnose production issues, own end-to-end deployments, and transform firefighting into scalable tooling within a small, high-performing team building RL and AI evaluation infrastructure.

What you'll do

  • Diagnose and resolve ambiguous technical problems from triage through completion
  • Own technical deployment requests from AI labs, data vendors, and internal teams
  • Build one-off tools and pipelines to address urgent customer problems with quick turnaround
  • Clarify underspecified requests and identify actual requirements through critical questioning
  • Coordinate with research and go-to-market teams to unblock critical deployments
  • Convert recurring manual work into reusable tools and documented processes

What they're looking for

  • Python
  • Docker
  • Linux environments
  • Debugging across code, data, and infrastructure
  • Benchmarks and evaluation frameworks
  • RL training and task design
  • Independent problem-solving in ambiguous situations
  • Production troubleshooting under time pressure

Benefits

  • Visa sponsorship available
  • On-site role in San Francisco with option of Singapore office
  • High-caliber, small team environment
  • Exposure to frontier AI labs and cutting-edge research infrastructure
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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 production issue where the root cause wasn't immediately obvious—how did you approach it?
  • Describe your experience building evaluation benchmarks or RL training tasks. What made them reliable and useful?
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