Machine Learning Scientist; Position

Our Client, a Biotech company, is looking for a Machine Learning Scientist (Contract Position) for their South San Francisco, CA location.

• 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.

• 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.
• 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.

• Health Benefits
• Referral Program
• Excellent growth and advancement opportunities

As an equal opportunity employer, ICONMA provides an employment environment that supports and encourages the abilities of all persons without regard to race, color, religion, gender, sexual orientation, gender identity or express, ethnicity, national origin, age, disability status, political affiliation, genetics, marital status, protected veteran status, or any other characteristic protected by federal, state, or local laws.