Machine Learning Engineer

Job Title: Machine Learning Engineer
Location: 1 DNA Way, South San Francisco, CA 94080
Duration: 12-month contract (possibility of extension)
Pay Rate: $47.35 – $75.00 per hour (W2)
Work Model: Hybrid

About the Role
Client’s Prescient Design team within the Research and Early Development (gRED) organization is seeking a Machine Learning Engineer to help advance structural and machine learning-based approaches for molecular design.

You will play a key role in developing and deploying advanced ML techniques for molecular optimization, property prediction, and active learning-driven drug discovery. This is an exciting opportunity to contribute to cutting-edge science while collaborating with top-tier researchers in computational biology, chemistry, and drug development.

Key Responsibilities

• Develop and deploy machine learning and Bayesian optimization workflows for molecular property prediction and optimization.
• Collaborate with scientists across Prescient Design and gRED to design, analyze, and optimize small and large molecule therapeutics.
• Engineer production-ready pipelines for probabilistic modeling, active learning, and molecular generative modeling.
• Support drug discovery initiatives by applying ML models to enable target-driven design campaigns.
• Contribute to existing research projects and help define new opportunities for machine learning in molecular science.

Required Qualifications

• PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or a related quantitative field
• – or – MS degree with 3+ years of relevant industry experience.
• Strong experience in software engineering and production-grade ML workflows using libraries such as PyTorch, Lightning, and Weights & Biases.
• Proven research track record (e.g., at least one high-impact first-author publication or equivalent).
• Excellent written, visual, and verbal communication skills with a collaborative mindset.

Preferred Skills & Experience
Candidates with strong expertise in one or more of the following areas are highly encouraged to apply (listed in order of importance):

• Molecular property prediction
• Probabilistic modeling and inference
• Bayesian optimization or active learning
• Production software engineering or pipeline optimization
• Cheminformatics

Additional desirable skills include:

• Experience with physical modeling methods (e.g., molecular dynamics).
• Familiarity with cheminformatics toolkits (e.g., RDKit).

Background in:

• De novo drug design
• Computational or medicinal chemistry
• Small molecule design
• Self-supervised or geometric deep learning
• Statistical modeling and data analysis
• Public portfolio (e.g., GitHub) demonstrating computational or ML projects.

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