Über
Machine Learning Scientist Location:
South San Francisco, CA 94080 Pay Rate:
$44.59 $47.59 per hour (Determined by experience) Contract Duration:
W2 Contract, 12 Month Contract with possibilities for extensions Work Model:
100% Onsite Role Overview BEPC is seeking a Machine Learning Scientist to support computational method development within a cutting-edge therapeutic discovery environment in South San Francisco, CA. This role sits within a Structure and Simulation team focused on advancing biomolecular design and accelerating therapeutic discovery through modern machine learning and scientific computing approaches. The ideal candidate will contribute to developing new ML-driven methodologies and deploy production-ready scientific software to support computational and experimental scientists. Key Responsibilities Scientific Method Development Develop new machine learning methodologies for understanding, scoring, ranking, generation, and design of biomolecules (especially proteins) Build predictive and generative models to prioritize promising molecular designs Support lab-in-the-loop workflows integrating computational and wet-lab experimentation Software Engineering & Deployment Engineer scalable scientific software and deploy usable workflows Follow best practices in: Version control Testing Modular code development Documentation Collaborate within a large shared codebase Deploy workflows on HPC and cloud platforms Deliver user-friendly web-based interfaces for medicinal chemists Cross-Functional Collaboration Partner with computational and experimental scientists Contribute to advancing biomolecular scientific understanding Communicate findings clearly to multidisciplinary teams Required Qualifications BS, MS, or PhD in life sciences, physical sciences, or computational field Expert-level proficiency in Python Experience with scientific software development Experience deploying workflows on HPC and/or cloud platforms Experience working in collaborative codebases (merge requests, code reviews, writing tests) Understanding of modern machine learning methods, including: Predictive models Generative models Active learning Strong communication and collaboration skills Self-starter with high motivation and independence Preferred Qualifications Public GitHub portfolio Experience with Rosetta, OpenMM, or computational chemistry tools 3+ years of industry experience Experience working with large chemical and biological datasets Graph-based data Sequence-based data Structure-based data Ideal Candidate Strong ML background applied to molecular design Comfortable working at the intersection of biology and computation Production-oriented engineer who values clean, deployable code Collaborative, yet capable of independent problem-solving Passionate about advancing therapeutic discovery
Sprachkenntnisse
- English
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