MindSource
Title:
Machine Learning Engineer Location:
South San Francisco, CA Position Type:
Contract Rate Range:
$37.44 - $83.44
MindSource is looking for a Machine Learning Engineer to join our client's team in South San Francisco, CA. They will be developing and deploying advanced computational methods for molecular design. This is a 12-month hybrid contract.
About the Role Build pipelines for
probabilistic molecular property prediction
and
Bayesian acquisition
to power active learning-driven drug discovery. Engineer workflows for
molecular generative modeling
and other innovative design approaches. Collaborate with machine learning scientists, engineers, computational chemists, and biologists. Partner with therapeutic development teams to analyze existing molecules and design new candidates. Contribute to ongoing initiatives while driving new research directions. Qualifications
PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or related quantitative field - OR MS + 3+ years of relevant industry experience. Demonstrated expertise in
production-ready ML workflows
(e.g., PyTorch + Lightning + Weights & Biases). Strong track record of achievement (e.g., high-impact first-author publication or equivalent). Excellent written, visual, and verbal communication skills. Preferred Experience
Knowledge of physical modeling (e.g., molecular dynamics) and cheminformatics (e.g., RDKit). Background 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, or statistical methods. Hands-on experience with
Python, PyTorch, Torch Geometric, PyTorch Lightning, RDKit, and BoTorch . Public portfolio of computational projects (e.g., GitHub).
Machine Learning Engineer Location:
South San Francisco, CA Position Type:
Contract Rate Range:
$37.44 - $83.44
MindSource is looking for a Machine Learning Engineer to join our client's team in South San Francisco, CA. They will be developing and deploying advanced computational methods for molecular design. This is a 12-month hybrid contract.
About the Role Build pipelines for
probabilistic molecular property prediction
and
Bayesian acquisition
to power active learning-driven drug discovery. Engineer workflows for
molecular generative modeling
and other innovative design approaches. Collaborate with machine learning scientists, engineers, computational chemists, and biologists. Partner with therapeutic development teams to analyze existing molecules and design new candidates. Contribute to ongoing initiatives while driving new research directions. Qualifications
PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or related quantitative field - OR MS + 3+ years of relevant industry experience. Demonstrated expertise in
production-ready ML workflows
(e.g., PyTorch + Lightning + Weights & Biases). Strong track record of achievement (e.g., high-impact first-author publication or equivalent). Excellent written, visual, and verbal communication skills. Preferred Experience
Knowledge of physical modeling (e.g., molecular dynamics) and cheminformatics (e.g., RDKit). Background 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, or statistical methods. Hands-on experience with
Python, PyTorch, Torch Geometric, PyTorch Lightning, RDKit, and BoTorch . Public portfolio of computational projects (e.g., GitHub).