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Chai Discovery

Research Engineer - Auto Research

San Francisco officefulltimemidAdded today

About this role

Chai Discovery seeks a Research Engineer to build agent-powered automation systems that accelerate the research workflow for molecular design. You'll develop frameworks and tooling that enable researchers and AI agents to autonomously run experiments, evaluate results, and iterate faster across the model lifecycle.

What you'll do

  • Develop and optimize agent-powered auto-research frameworks to improve model training velocity and inference cycles
  • Build tooling, datasets, and workflows enabling agents and researchers to run experiments with minimal manual overhead
  • Automate research workflows across training, evaluation, and deployment phases
  • Design software abstractions and systems that researchers and agents can build upon
  • Collaborate with AI/ML research teams to identify and eliminate bottlenecks in the research loop

What they're looking for

  • Python
  • PyTorch or JAX
  • Software system design and architecture
  • Agent-powered systems development
  • ML research workflow automation
  • Experimental design and evaluation
  • API and tooling design
  • Distributed systems or computational optimization

Benefits

  • Work on frontier AI and biology applications
  • Collaborate with world-class research team
  • High-velocity, ownership-focused culture
  • Competitive compensation
  • San Francisco office location
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Chai Discovery

Chai Discovery builds AI-powered tools for molecular biology and drug discovery, enabling scientists to design new therapeutic molecules through advanced AI systems. The company is hiring Security Engineers, Design Engineers, AI Research Engineers, Infrastructure Engineers, and Product Software Engineers to develop and scale its platform.

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Likely interview questions

  • Describe a time you built tooling or abstractions that other engineers or researchers used. How did you validate they were solving the right problem?
  • Walk us through your experience with agent-powered systems. What challenges did you encounter and how did you solve them?
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