Senior Machine Learning Scientist

San Francisco

Full Time Senior-level / Expert
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Senior Machine Learning Scientist

Atomwise is the leading artificial intelligence (AI) drug discovery company, based in San Francisco, CA. We discover and develop small molecules that will improve human health and agricultural productivity. 

Our team has over 35 Ph.D. scientists who contribute to a collaborative academic-like culture that fosters robust scientific and technical discussion. We strongly believe that data wins over opinions, and aim for as little dogma as possible in our decision-making. Our team members have expertise in a wide range of disciplines--from computational chemistry and structural biology to cloud-native best practices--and we regularly have internal seminars open to anyone interested in learning about these topics.

Our ml.research team is responsible for developing new tools and techniques to be used by our applied scientists. We are looking for a senior machine learning scientist with significant research experience to join our team. As a member of our team, you will:

  • Perform cutting-edge research on topics including generative models, self-supervised learning, out-of-distribution generalization, and structure-based graph neural networks.
  • Interact with our academic partners to develop and implement new ideas related to structure-based drug discovery.
  • Write and publish papers resulting from academic collaborations or internal research.
  • Translate research ideas to our internal framework, and evaluate with a skeptical eye, and deliver new tools to our applied machine learning group.

Our Machine Learning team is small and growing quickly. As a result, there are plenty of opportunities to have a big impact on our success.

Required qualifications:

  • Ph.D. or M.Sc. in computer science, statistics, data science, or related field
  • 5+ years of extensive practical experience and proven track record of developing, implementing, debugging, and extending machine learning algorithm
  • Knowledge of modern neural network frameworks such as PyTorch, TensorFlow, or JAX
  • Strong analytical and statistical skills
  • Scientific rigor, healthy skepticism, and detail-orientation in training and analyzing machine learning models
  • A strong publication record at the intersection of machine learning and biology, chemistry, or physics

Preferred qualifications:

  • Experience with graph neural network frameworks such as PyTorch Geometric or Deep Graph Library
  • Familiarity with generative models or self-supervised learning
  • Understanding of equivariant or other physics-inspired neural networks
  • Experience with cloud computing environments (AWS/Azure/GCE)

Please apply with a resume and cover letter.

Compensation and Benefits:

  • Great, world-class team of colleagues – scientists from a variety of backgrounds (chemistry, medicine, biology, physics, CS/ML)
  • Stock compensation plan – you’ll be an Atomwise co-owner
  • Platinum health, dental, and vision benefits
  • 401k with 4% match
  • Funding for professional development and conference attendance
  • Flexible work schedule
  • Generous parental leave

Atomwise is an equal opportunity employer and strives to foster an inclusive workplace.  Our mission is to develop better medicines faster, and we know that we need a diverse team to develop medicines that serve diverse populations.  Accordingly, Atomwise does not make any employment decisions (including but not limited to, hiring, compensation, and promotions) on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, veteran status, disability status, or any other characteristics protected by applicable federal, state, and local law.

We strongly encourage people of diverse backgrounds and perspectives to apply.

Atomwise is not currently offering visa sponsorships for any position. Please only apply if eligible to work in the U.S.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.



 

Job region(s): North America
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