Machine learning Scientist (Computational Chemistry/Proteins) - Full-time / Part-time
| Verified Pay check_circle | Provided by the employer$50 per hour |
|---|---|
| Hours | Full-time, Part-time |
| Location | South San Francisco, CA South San Francisco, California open_in_new |
About this job
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 GitHub.
- 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.
If interested, please
send us your updated resume at
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