Scientist, Machine Learning
Atomai
South San Francisco
At Atomic AI, we build artificial intelligence to pioneer new frontiers in drug discovery. Our unique R&D platform, an early version of which was featured on the cover of Science, provides new strategies to treat previously undruggable diseases by targeting RNA. We continue to advance this platform by developing new machine learning methods and unique foundation models fueled by our large-scale, in-house experimental data collection. We are an interdisciplinary team of scientists and engineers and believe our people are our greatest strength and the key to our success.
The opportunity
As a full-time Scientist on the Machine Learning team, you will work closely with engineers and experimental scientists to advance our technology platform for RNA structure prediction, target identification, and early drug discovery. You will co-lead the development and evaluation of the machine learning pipeline. You will contribute new ideas and realize their potential as part of a continuously advancing state-of-the-art platform. You will proactively shape the directions of the machine learning efforts and those of the whole company.
Primary responsibilities
- Design and develop novel machine learning models for RNA structure prediction and drug targeting.
- Evaluate and advance the state of the art of our structure prediction platform.
- Collaborate with our wetlab team on the targeted acquisition of experimental data to improve our machine learning models.
- Develop high-quality code in a team setting.
- Analyze, interpret, and organize results and present progress to colleagues in regular research meetings.
- Work within a collaborative, high-caliber, interdisciplinary team and proactively shape the scientific and strategic vision of the company.
About you
- Ph.D., M.Sc., or M.Eng. in Computer Science, Physics, Applied Mathematics, Materials Science, Computational Biology, or related field.
- 4+ years of experience developing machine learning methods for scientific applications.
- Foundational knowledge of machine learning and underlying mathematical concepts.
- Proficiency in Python and deep learning frameworks (e.g., JAX, PyTorch).
- Excellent presentation and writing skills, able to clearly communicate technical information to colleagues.
Pluses
- Publications at major machine learning conferences or in major scientific journal
- Research experience related to structural biology, molecular design, and drug discovery.
- Foundational knowledge of physics, chemistry, and molecular biology.
- Demonstrated ability to develop performant code.
Salary Range (all levels): $170,000/year to $220,000/year + equity + benefits. This range reflects variations in seniority, expertise, and skills.
Atomic AI is committed to equal employment opportunity regardless of race, color, ancestry, national origin, religion, sex, age, sexual orientation, gender identity and expression, marital status, disability, or veteran status.
Similar jobs
Scientist, Metabolism
Company infoNewLimit is a biotechnology company working to radically extend human healthspan. We’re developing medicines to treat age-rel...
Senior Scientist / Scientist, In Vivo Pharmacology
Company infoNewLimit is a biotechnology company working to radically extend human healthspan. We’re developing medicines to treat age-rel...
Scientist, Drug Metabolism And Pharmacokinetics (Dmpk)
About Us Kardigan is a heart health company working to make cardiovascular disease preventable, curable and no longer the leading cause o...
Scientist, Lnp-Drug Product Development
Company infoNewLimit is a biotechnology company working to radically extend human healthspan. We’re developing medicines to treat aging b...
Research Associate Ii/ Senior Research Associate, Therapeutics
Who Are We?Our company is based on the science of our founder, Stanley Qi, one of the original CRISPR co-inventors who then furthered the...
Principal Scientist, Analytical Development
About the roleSana Biotechnology is growing, and we are hiring new team members who believe in the potential of engineered cells as medic...