ML Research Scientist
San Francisco, California, United States
- Pay
- Pay not listed
- Work setting
On-site — work setting source
Work setup in the employer’s listing: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s listing: FullTime
From the employer’s posting
What you’ll work on
Full postingDevelop generative models for protein conformational ensembles trained on simulation and experimental data
Design scalable training pipelines for high-throughput experimentation
Build novel ML architectures for ensemble-based affinity prediction and generative chemistry
From the employer’s posting
What you will do Develop generative models for protein conformational ensembles trained on simulation and experimental data Build novel ML architectures for ensemble-based affinity prediction and generative chemistry
Adapt methods from research literature to disordered protein targets Design scalable training pipelines for high-throughput experimentation Collaborate with computational and experimental scientists to validate models against biophysical data
Develop generative models for protein conformational ensembles trained on simulation and experimental data Build novel ML architectures for ensemble-based affinity prediction and generative chemistry Adapt methods from research literature to disordered protein targets
What you’ll bring
All qualificationsCore experience
- Deep understanding of modern generative models with application to the sciences
- Experience with molecular simulation and protein modeling
Qualification wording
Deep understanding of modern generative models with application to the sciences
Experience with molecular simulation and protein modeling
Education & alternatives
Preferred - MS or PhD in machine learning with application to computational science - Strong publication record
Posting record
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- First seen by us
- Oct 18, 2026 · today
- Recorded sightings
- 1
- Employer says posted
- Sep 30, 2026
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Job description
Topos Bio
Topos Bio is developing computational methods to drug intrinsically disordered proteins.
We're seeking a research scientist to develop and optimize ML methods that bridge simulation, protein modeling, and small molecule design. This role requires both strong machine learning foundations and the ability to work at the interface of computational biology, structural modeling, and chemistry.
What you will do
Develop generative models for protein conformational ensembles trained on simulation and experimental data
Build novel ML architectures for ensemble-based affinity prediction and generative chemistry
Adapt methods from research literature to disordered protein targets
Design scalable training pipelines for high-throughput experimentation
Collaborate with computational and experimental scientists to validate models against biophysical data
What we are looking for
Strong foundation in machine learning theory and implementation
Deep understanding of modern generative models with application to the sciences
Background in computational sciences involving biology and chemistry
Preferred
MS or PhD in machine learning with application to computational science
Strong publication record
Experience with molecular simulation and protein modeling
Source details
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- Pay
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- Location & working pattern
San Francisco, California, United States
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- Work authorization
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