Our client, a world leader in diagnostics
and life sciences, is looking for a "Machine Learning Engineer” based out of South San Francisco, CA.
Job Duration: Long Term Contract (Possibility Of
Further Extension)
Company Benefits: Medical, Dental,
Vision, Paid Sick leave, 401K
Job Description:
The successful candidate will manage projects deploying new techniques for machine learning based molecular optimization for the analysis and design of small and large molecule drugs within target-driven design campaigns. Special focus will be given to engineering pipelines for probabilistic molecular property prediction and Bayesian acquisition for active learning based drug discovery.
- Additional activities may extend to include engineering pipelines for molecular generative modeling
- You will join Prescient Design within the Computational Sciences organization in gRED. Your peers will be machine learning scientists, engineers, computational chemists, and computational biologists.
- You will closely collaborate with scientists within Prescient and across gRED.
- You will develop machine learning and Bayesian optimization workflows to analyze existing, and design new, small and large molecules.
- Demonstrated experience with machinelearning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases)
- Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit)
- Previous focus on one or more of these areas: molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, statistical methods.
- Public portfolio of computational projects (available on e.g. GitHub).
Qualifications:
If interested, please send us your updated
resume at
[email protected]/[email protected]
Skills Required
- PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics OR MS degree with 3+ years industry experience
- Experience building production-ready ML workflows using PyTorch, PyTorch Lightning, and Weights & Biases
- Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits such as RDKit
- Domain experience in molecular property prediction, computational chemistry, de novo drug design, or medicinal chemistry
- Experience with probabilistic modeling, Bayesian optimization, active learning, self-supervised learning, or geometric deep learning
- Public portfolio of computational projects (e.g., GitHub)
What We Do
Dawar Consulting Inc. is a professional services and staff augmentation firm specializing in IT consulting, workforce solutions, and HCM/HRIS services. They provide technology and business consulting, project delivery, and IT support to help clients achieve their strategic goals. With expertise across IT, Engineering, and Finance, they deliver best-in-class workforce solutions and innovative strategies to drive operational efficiency and business success.
