ML Research Scientist
Reads like a ML Engineer role: putting models into production.
San Francisco Office · San Francisco, California, United States
- Pay
- Pay not listedSimilar roles state $165K–$240K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering — team source
Department: Engineering
From the employer’s posting
What you’ll work on
Full postingDevelop generative models for protein conformational ensembles from simulation and experimental data
Build ML architectures for ensemble-based affinity prediction and generative chemistry
Adapt research literature methods to disordered protein targets
Design scalable training pipelines for high-throughput experimentation
Validate models against biophysical data with computational and experimental scientists
From the employer’s posting
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 you’ll bring
All qualificationsCore experience
- Strong foundation in machine learning theory and implementation
- Deep understanding of modern generative models applied to the sciences
- Background in computational sciences involving biology and chemistry
- Strong publication record
- Experience with molecular simulation and protein modeling
Preferred experience
- MS or PhD in machine learning with application to computational science
Qualification wording
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
Strong publication record
Experience with molecular simulation and protein modeling
Preferred MS or PhD in machine learning with application to computational science
Education & alternatives
Preferred - MS or PhD in machine learning with application to computational science - Strong publication record
Posting record
Report an errorActively hiringFirst seen by us 11 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 18, 2026 · 11 days ago
- Recorded sightings
- 13
- Last seen by us
- Oct 29, 2026
- Employer says posted
- Sep 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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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
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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