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 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)
Compensation & Benefits at insitro
Our target starting salary for successful US-based applicants for this role is $211,000 - $283,000. 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
New hire stipend for home office setup
Monthly cell phone & internet stipend
Access to free onsite baristas and daily lunch for employees who are either onsite or hybrid
Access to a free commuter bus network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area
insitro is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams–grounded in a wide range of expertise and life experiences–and work even harder to ensure those teams thrive in inclusive, growth-oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process.
Please be aware of recruitment scams: we never request payments, all recruitment communications are from @insitro.com, and if in doubt, contact us at info@insitro.com.
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About insitro
insitro is a drug discovery and development company using machine learning (ML) and data at scale to decode biology for transformative medicines. At the core of insitro’s approach is the convergence of in-house generated multi-modal cellular data and high-content phenotypic human cohort data. We rely on these data to develop ML-driven, predictive disease models that uncover underlying biologic state and elucidate critical drivers of disease. These powerful models rely on extensive biological and computational infrastructure and allow insitro to advance novel targets and patient biomarkers, design therapeutics and inform clinical strategy. insitro is advancing a wholly owned and partnered pipeline of insights and therapeutics in neuroscience and metabolism. Since launching in 2018, insitro has raised over $700 million from top tech, biotech and crossover investors, and from collaborations with pharmaceutical partners. For more information on insitro, please visit www.insitro.com.
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