Machine learning Scientist; Computational Chemistry/Proteins at Dawar Consulting South San Fra
Listed on 2026-07-26
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Software Development
Machine Learning/ ML Engineer, Data Scientist, AI Engineer (Applied/Software)
Machine Learning Scientist (Computational Chemistry/Proteins)
Our client, a world leader in biotechnology and life sciences, is looking for a Machine Learning Scientist (Computational Chemistry/Proteins) based out of South San Francisco, CA. Job Duration:
Long Term Contract (Possibility Of Extension) Pay Rate: $50/hr on W2
Company Benefits:
- Medical
- Dental
- Vision
- Paid Sick leave
- 401K
Work as a ML scientist to develop new scientific methodology for the understanding, scoring, ranking, generation, and design of biomolecules, especially proteins. Work as an engineer of scientific software, to produce usable, deployable code for these new methods to power the lab-in-the-loop. Use software best practices (version control, testing, modular code development, documentation, etc.) to collaborate on a large codebase with our team of methods developers.
Deploy workflows on HPC and cloud platforms and deliver user-friendly web-based interfaces to medicinal chemists across gRED.
Desired
Qualifications:
BS, MS, or PhD degree in a life or physical science or a computational field. Expert in Python and experience with scientific software development. Experience with deploying software workflows on cloud and/or HPC platforms. Experience working on collaborative code bases, including merge requests, code review, writing tests etc. Basic understanding of modern machine learning methods including predictive models, generative models, and active learning as applied to molecular generation and optimization.
Preferred Qualifications:
Candidates may additionally have, but are not required to have:
Public portfolio of projects available on Git Hub. Experience with Rosetta, OpenMM, and/or computational chemistry codes. 3+ years of industry experience. Extensive experience working with large chemical and biological datasets, including graph, sequence, and structure-based data.
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