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Scientist, Machine Learning

Atomic AI
Posted 6 months ago, valid for 12 days
Location

South San Francisco, CA 94080, US

Salary

$170,000 - $230,000 per year

Contract type

Full Time

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Sonic Summary

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  • Atomic AI is seeking a full-time Scientist for their Machine Learning team to advance their technology platform for RNA structure prediction and drug discovery.
  • Candidates should have a Ph.D., M.Sc., or M.Eng. in a relevant field and at least 4 years of experience developing machine learning methods for scientific applications.
  • The role involves designing and developing novel machine learning models, collaborating with experimental scientists, and presenting research findings to colleagues.
  • Proficiency in Python and deep learning frameworks is required, along with excellent communication skills for conveying technical information.
  • The salary range for this position is between $170,000 and $230,000 per year, plus equity and benefits.

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.




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By applying, a Atomic AI account will be created for you. Atomic AI's Privacy Policy and Terms & Conditions will apply.

SonicJobs' Terms & Conditions and Privacy Policy also apply.