Senior Applied Machine Learning Scientist

The Opportunity

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.

• 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

• 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

• 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 Tensor Flow)
• Demonstrated expertise in developing geometric deep learning and/or generative AI methods for small molecules
• Proficiency with at least 1–2 cheminformatics toolkits (RDKit, Open Eye, 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)

Our target starting salary for successful US‑based applicants for this role is $185,000 – $235,000. To determine starting pay, we consider multiple job‑related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.

This role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies.

In addition, insitro also provides our employees:

• 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
• N…