Machine Learning Scientist
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Job Location:
South San Francisco, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
8 hours ago
Vacancies:
1 Vacancy
Job Summary
Duration 12 months
Location - South SFO CA
Machine Learning Scientist (Contract Position)
In the Structure and Simulation team within Prescient Design we develop modern computational methods to accelerate therapeutic discovery across Genentech Research and Early Development (gRED). Methods we deploy propose new molecules score designs to prioritize the most promising compounds generate biological hypotheses through exploratory simulation accelerate physics-based calculations and more.
We are seeking a highly motivated Machine Learning Scientist to join our team to develop new scientific methodology and produce and deploy workflows usable by computational and ultimately wet-lab scientists. The successful candidate will collaborate extensively with computational and experimental researchers within Prescient Design and across gRED to advance our scientific understanding of biomolecules.
The Role
Work as a machine learning 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 and Roche.
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.
Highly-motivated and independent self starter that is eager to collaborate.
Excellent communication and interpersonal skills.
Basic understanding of modern machine learning methods including predictive models generative models and active learning as applied to molecular generation and optimization.
Additional 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
In the Structure and Simulation team within Prescient Design we develop modern computational methods to accelerate therapeutic discovery across Genentech Research and Early Development (gRED). Methods we deploy propose new molecules score designs to prioritize the most promising compounds generate biological hypotheses through exploratory simulation accelerate physics-based calculations and more.
We are seeking a highly motivated Machine Learning Scientist to join our team to develop new scientific methodology and produce and deploy workflows usable by computational and ultimately wet-lab scientists. The successful candidate will collaborate extensively with computational and experimental researchers within Prescient Design and across gRED to advance our scientific understanding of biomolecules.
The Role
Work as a machine learning 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 and Roche.
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.
Highly-motivated and independent self starter that is eager to collaborate.
Excellent communication and interpersonal skills.
Basic understanding of modern machine learning methods including predictive models generative models and active learning as applied to molecular generation and optimization.
Additional 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
Key Skills
- Laboratory Experience
- Immunoassays
- Machine Learning
- Biochemistry
- Assays
- Research Experience
- Spectroscopy
- Research & Development
- cGMP
- Cell Culture
- Molecular Biology
- Data Analysis Skills
