The Opportunity

Machine learning lies at the core of insitro’s approach to rethinking drug development. The Molecular ML team supports ongoing therapeutic pipeline projects and bridges the gap between classical and machine learning approaches to drug discovery. Our responsibilities include the support of therapeutics projects in the development of clinical molecules, design and execution of physics- and ML-driven solutions for accelerating key problems in drug discovery, and creation of APIs and tooling for making the entire process reproducible and faster over time.

You will join an interdisciplinary team of modelers and machine learning scientists to help accelerate drug discovery. You will report to the head of Computational Drug Design in the Molecular ML group, and will help bridge the gap between therapeutic project needs and our chemistry platform capabilities. You will bring a strong foundation in traditional modeling and drug discovery approaches and become a domain expert in the application of machine learning for drug discovery. This is a hybrid position with the expectation of working 3 days per week onsite in our South San Francisco office.

Responsibilities

• Support drug discovery projects in numerous therapeutic areas of high unmet medical need
• Deploy industry-leading ML- and physics-based modeling tools to those drug discovery projects
• Collaborate with our machine learning, automation, biology, and chemistry groups to develop new tools for hit discovery and lead optimization
• Shape our platform and our team’s culture with your ideas and experience

About You

• PhD or Masters in computational chemistry, cheminformatics, molecular simulations, or machine learning or equivalent relevant experience; PhD (0-2 years), Masters (3+ years)
• Passionate about developing state-of-the-art methods for accelerating drug discovery so we can improve the speed at which we can bring treatments to patients
• Track-record of uncovering computational solutions to problems in chemistry, biology, drug discovery, and related disciplines
• Technical proficiency with software and methods development

Compensation & Benefits at insitro

Our target starting salary for successful US-based applicants for this role is $140,000 - $180,000. 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 (insitro pays 100% of premiums for employees on our base plans), as well as mental health and well-being support
• Open, flexible vacation policy
• Paid parental leave
• 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
• Access to free onsite fitness center
• Commuter benefits

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.

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