Machine Learning Scientist, Molecular Interactions

Terray Therapeutics

1d • $147,000 - $227,850

About The Position

Terray Therapeutics is seeking an ML scientist to join our team focused on extending structure informed generative models of potency to causal models of in-vivo molecular interactions. The role involves a significant amount of creative latitude, and the opportunity to directly impact real drug programs informed by large, proprietary datasets. Successful applicants must possess a comprehensive knowledge of how today’s structure informed models work, where they need improvement, and an enthusiasm to pursue genuinely novel modeling concepts while advancing measurable improvements. An opinionated perspective on the tension between the bitter lesson and physical constraints is a plus.
The responsibility of this role is experimentally realizing improvements in the selectivity, generality, scope, and/or accuracy of intermolecular interactions designed with ML models over our current state of the art through novel architectures.

Requirements

• A comprehensive understanding of existing work on ML modeling of in-vivo molecular interactions, and an informed desire to pursue open avenues for further improvement.
• Mastery of modern ML model development, training at scale and interest in developing novel approaches which can bootstrap to greater fidelity at inference time.
• Curious interest in novel approaches, and the plasticity to possibly attempt highly speculative but novel research.
• Basic familiarity with modern ML ops.
• Basic familiarity with other classes of other ML models implicated in Drug Design, and the ability to use these models to complete the design process.
• Ability to collaborate on a diverse team, and couple to deployed workflows for drug design scientists.
• Applicants must have github, proof of relevant work, or a one-page writeup of experience applying molecular modeling to a scientific problem that is verifiable.

Nice To Haves

• An opinionated perspective on the tension between the bitter lesson and physical constraints is a plus.

Responsibilities

• Experimentally realizing improvements in the selectivity, generality, scope, and/or accuracy of intermolecular interactions designed with ML models over our current state of the art through novel architectures.

Benefits

• Participation in the Company’s stock option plan
• A 3% retirement safe harbor contribution
• Fully paid health, dental, vision insurance for our employees, spouse, partner and families
• Above-market life insurance
• Disability coverage

What This Job Offers

• Job Type: Full-time
• Career Level: Senior