insitro is a physical AI company dedicated to unlocking causal human biology and accelerating the delivery of better medicines to patients. Our unique Virtual Human™ platform identifies novel, high-impact genetic intervention points, which our TherML™ platform translates into therapeutics—whether small molecules, biologics, or oligos. With multiple programs in metabolic disease and neuroscience advancing toward the clinic, and our first IND submission slated for the second half of this year, we are at a pivotal inflection point.
We are seeking a Senior Scientist to join our cardiac disease biology team. In this role, you will own the development and application of iPSC-derived cardiomyocyte (iPSC-CM) models to study the genetic and molecular underpinnings of cardiovascular disease — working at the intersection of human stem cell biology, cardiac physiology, and scalable experimental platforms to generate the datasets that fuel our ML-driven drug discovery efforts. You will design and execute experiments, optimize differentiation and assay workflows, and partner closely with ML scientists, computational biologists, and chemists to connect cellular phenotypes to disease mechanisms and therapeutic targets. Based in South San Francisco, this position reports directly to **Vice President, Cardiometabolic Disease - Translational Genetics** and requires you to be onsite at our South San Francisco office **5 days per week**.
Our target starting salary for successful US-based applicants for this role is $156,000 - $166,000. 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:
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_](http://insitro.com) _, and if in doubt, contact us at_ __info@insitro.com__ _._#LI-OnsiteAbout 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](http://www.insitro.com).
Insitro is an AI-driven drug discovery and development company leveraging machine learning and large-scale biological data to create better medicines faster.
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