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Research Scientist, Pre-training Research

Chai Discovery

San Francisco, CAFull-timePosted todayStill listed today

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At a glance

Compensation
No compensation found
Location
San Francisco, CA
Schedule
Full-time
Work Authorization
Not specified

Olive lists jobs from US employers, including remote roles you can work from the United States.

Job overview

Chai Discovery is hiring research scientists to work on foundation model pre-training for models of molecular structure and interaction. The role focuses on architectural components, training objectives, and data mixes, adapting ideas from frontier language, image, and video models. Researchers will have access to compute and a team to advance the models’ applications in therapeutic engineering.

Skills & qualifications

RequiredNice to have

Skills

Generative ModelingModel ArchitectureScaling LawsLarge Model Training

Qualifications

Strong Research Track RecordCS, ML, Math, or Physics Theoretical Grounding

Full job description

About Chai Discovery Chai Discovery builds the computer-aided design suite for molecules. Eli Lilly, GSK, Pfizer, Bristol Myers Squibb, argenx and Novartis all use Chai to power drug discovery programs. AI is reinventing life sciences the same way it reinvented software engineering, and our team is building that future. We train frontier models that learn the underlying foundations of biochemical structure and interaction. We pair this with a computational workspace to iterate on molecules from idea to medicine, helping scientists move faster and develop medicines beyond the reach of existing methods. We value diverse perspectives and are ready to find greatness in unexpected places. About the role We are hiring research scientists to work on foundation model pre-training. Our models learn the foundations of molecular structure and interaction, and their success depends on carefully designed architectural components, training objectives, and data mixes. At Chai, pre-training research is largely domain-agnostic: understanding model architecture, reading training curves, and knowing when to move between phases of a recipe. We are biased toward simplification, generality, and scale. We adapt the best ideas from frontier language, image, and video models into our domain. Our models have moved beyond protein structure prediction into real-world therapeutic engineering, and you will have the compute and the team to push that further. About you We look for researchers with a strong track record, which can take many forms: getting a model to work, developing impactful theory, or making a substantial contribution to the field. In particular we look for:

  • Depth in generative modelling, model architecture, or scaling laws.

  • Hands-on experience designing and training large models.

  • Theoretical grounding in CS, ML, math, or physics.

We are happy to bet on rising talent. We offer The opportunity to work at the vanguard of AI research and frontier biology, with world-class people, on a mission that matters. We protect and promote a culture of high velocity and ownership. We compensate our team accordingly.

How this job compares

Set against the 2,170 open Research Scientist jobs on Olive Jobs, counted November 4, 2026.

Pay
  • This postingNot listed
  • California median$183–273K/yr498 salary ranges
  • US median$34–45/hr42 hourly ranges
Openings
Olive Jobs lists 723 open Research Scientist jobs in California and 2,170 across the US. 3% of them nationwide are listed as remote or flexible.
Hiring the most in California
Experience
Research Scientist postings that state a minimum ask for 4 years of experience at the median; the middle half ask for 2 to 6 years (1,146 postings).
Education
1,110 of the 2,170 open Research Scientist jobs (51%) ask for a bachelor's degree or higher.

Counted across the open, public US postings on Olive Jobs on November 4, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.

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