(Senior) Applied Machine Learning Scientist

Insitro•South San Francisco, CA

41d•$185,000 - $235,000•Hybrid

About The Position

Machine learning lies at the core of insitro's approach to rethinking drug development. We've built a ChemML Platform that uses AI to accelerate small molecule discovery from hit-finding through lead optimization. Our innovative approach enables us to rapidly design drugs against internally discovered novel AI targets, with models powered by some of the industry's largest and most comprehensive datasets: Billions of proprietary binding affinity measurements Hundreds of thousands of ADMET and In Vivo PK measurements Experimental data generated through our AI-driven DMTL optimization cycles We're looking for an Applied Machine Learning Scientist who will be hands-on using our ChemML platform to drive design on multiple therapeutic programs while directly contributing to the development of our ChemML platform and ML models. This role offers an opportunity to translate cutting-edge ML into real-world impact on drug discovery. This role will report to the Senior Manager, Molecular Machine Learning and is a flexible hybrid (2 days per week in office) or remote (1 week per quarter on-site) role.

Requirements

PhD in computational chemistry, computer science, machine learning, cheminformatics, or related field
3+ years of industry experience applying machine learning to small molecule drug discovery
Strong programming skills in Python and modern deep learning frameworks (PyTorch or TensorFlow)
Demonstrated expertise in developing geometric deep learning and/or generative AI methods for small molecules
Proficiency with at least 1-2 cheminformatics toolkits (RDKit, OpenEye, or Schrödinger)
Excellent communication skills with ability to translate technical concepts to diverse audiences
A solid familiarity with small molecule drug discovery process and design methods (e.g., SAR, MPO, DMTL cycles, ADMET assays, in silico enumeration)

Responsibilities

Develop insitro's ChemML Platform:
Work with industry-leading ADMET/In Vivo PK datasets to train and finetune foundation chemistry AI models
Develop small molecule generative AI and enumeration methods conditioned using our ADMET/affinity models for multi-parameter optimization (MPO)
Collaborate with software engineering teams to build robust pipelines for active/iterative learning and automated DMTL cycles
Directly shape the roadmap for our ChemML platform's evolution
Integrate AI to Drive Our Small Molecule Discovery Programs:
Collaborate cross-functionally with machine learning scientists, medicinal chemists, and computational chemists to design and prioritize novel compounds using model outputs
Apply predictive models and AI-enabled design methods to solve real molecular optimization challenges across multiple therapeutic programs
Track performance of our binding affinity, selectivity, and ADMET/PK models on discovery programs and translate those insights into model improvements
Communicate model capabilities, results, limitations, and recommendations clearly to diverse stakeholders in regular discovery team meetings

Benefits

401(k) plan with employer matching for contributions
Excellent medical, dental, and vision coverage as well as mental health and well-being support
Open, flexible vacation policy
Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc)
Quarterly budget for books and online courses for self-development
Support to attend professional conferences that are meaningful to your career growth and role's responsibilities
New hire stipend for home office setup
Monthly cell phone & internet stipend
Access to free onsite baristas and cafe with daily lunch and breakfast for employees who are either onsite or hybrid
Access to free onsite fitness center for employees who are either onsite or hybrid
Access to a free commuter bus and ferry network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area