Atomwise-posted 8 months ago
$175,000 - $260,000/Yr
Full-time • Entry Level
San Francisco, CA
Administrative and Support Services

Atomwise is a TechBio company leveraging AI/ML to revolutionize small molecule drug discovery. The Atomwise team invented the use of deep learning for structure-based drug design; a core technology of Atomwise's best-in-class AI discovery and optimization engine, which is differentiated by its ability to find and optimize novel chemical matter. The company's belief is that structurally novel chemical matter increases the likelihood of developing first-in-class and best-in-class medicines that have the potential to transform patient care. Atomwise has extensively tested its discovery and optimization engine, delivering hit ID success in over 230 academic and collaboration projects-to-date that cover a wide breadth of protein classes and numerous “hard-to-drug” targets. Atomwise is building a pipeline of small-molecule drug candidates with first-in-class and best-in-class potential in immunology.

  • Develop and implement novel deep learning models for molecular property prediction, design and optimization, and virtual screening.
  • Design and execute experiments to evaluate the performance of generative models in generating chemically valid and biologically relevant molecules.
  • Collaborate with cross-functional teams of chemists, biologists, and software engineers to integrate ML models into drug discovery pipelines.
  • Conduct in-depth analyses of model outputs and performance metrics to identify areas for improvement.
  • Stay up-to-date with the latest advancements in machine learning and computational chemistry, and contribute to the scientific community through publications and presentations.
  • Contribute to the development of scalable and efficient machine learning infrastructure by productionizing research for at-scale use by drug discovery teams.
  • Explore and implement innovative techniques for multi-objective optimization, property prediction, and active learning within the context of generative modeling for drug design.
  • Ph.D. in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or a related field.
  • Hands-on experience with graph neural networks, multi-task learning, active learning strategies, multi-objective optimization, and generative modeling, in particular their application to problems in small molecule drug discovery.
  • Proficiency in designing and implementing novel machine learning architectures in PyTorch.
  • Strong problem-solving and analytical skills, with a demonstrated ability to conduct independent research.
  • Experience with collaborative software engineering and developing high quality, maintainable code in a large codebase.
  • Demonstrable communication and collaboration skills.
  • A strong publication record in relevant conferences and journals is highly desirable.
  • Experience with 3D generative modeling and knowledge of protein-ligand interactions.
  • Experience in optimizing models for large-scale training and inference.
  • Familiarity with cheminformatics tools and libraries (e.g., RDKit).
  • Competitive salary, commensurate with experience.
  • Stock compensation plan - you'll be an Atomwise co-owner.
  • Platinum health, dental, and vision benefits for you and your dependents.
  • 401(k) retirement plan with generous company match (up to 4%).
  • Flexible paid time off (PTO), 13 paid holidays, and wellness breaks for employees to spend time with their loved ones and recharge.
  • Health Savings and Flexible Spending Account options to help save money on healthcare, daycare, and commuting.
  • Employee Assistance Program (EAP) and Pet Insurance.
  • Funding for professional development and conference attendance.
  • Flexible work schedule.
  • Generous paid parental leave.
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