Machine Learning Engineer
Location: 1 DNA Way, South San Francisco, CA 94080
Duration: 1-Year Contract (with possibility for extension or conversion)
Overview
An innovative research and development team is seeking a Machine Learning Engineer to develop structural and machine learning-based methods for molecular design. The position focuses on molecular optimization for both small and large molecule drugs.
Key focus areas include:
- Probabilistic molecular property prediction
- Bayesian acquisition for active learning-based drug discovery
- Molecular generative modeling (potential involvement)
Key Responsibilities
- Collaborate with computational scientists, engineers, chemists, and biologists across multidisciplinary teams.
- Develop machine learning and Bayesian optimization workflows to analyze existing and design new molecular structures.
- Engineer pipelines for probabilistic property prediction, Bayesian acquisition, and generative modeling.
- Partner closely with small molecule and protein therapeutic development teams.
- Contribute to ongoing projects and propose innovative new initiatives.
Required Qualifications
- PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics or a related quantitative field
- — OR — MS degree with 3+ years of industry experience.
- Proficiency with production-ready ML workflows (e.g., PyTorch, PyTorch Lightning, Weights & Biases).
- Track record with at least one high-impact first-author publication or equivalent achievement.
- Strong written, visual, and oral communication skills.
Desired Qualifications
- Experience with molecular dynamics, physical modeling, and cheminformatics toolkits like rdkit.
- Expertise in molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, and statistical methods.
- Public computational project portfolio (e.g., GitHub).
