Senior Scientist, Cardiac Modeling Insitro - South San Francisco, CA,

Senior Scientist, Cardiac Modeling

insitro is a data-driven drug discovery and development company that leverages machine learning and high-throughput biology to transform the way medicines are created to help patients.

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.

iPSC-CM Platform & Model Development

• Differentiation & QC:
  Lead the generation, characterization, and maintenance of iPSC lines and their differentiation into cardiomyocytes, with rigorous quality control at each stage of the pipeline
• 2D & 3D Formats:
  Design, implement, and optimize iPSC-CM assays in both 2D monolayer and 3D/engineered heart tissue (EHT) formats, with a focus on scalability and data quality
• Genetic Disease Models:
  Build and apply isogenic iPSC-CM disease models — including CRISPR-edited lines carrying disease-associated variants — to study cardiovascular pathophysiology

Functional Assays & Disease Modeling

• Functional Readouts:
  Develop readouts of cardiomyocyte biology relevant to disease —contractility, electrophysiology, calcium handling, stress responses — in formats compatible with high-throughput data acquisition
• Rigor & Reproducibility:
  Drive documentation, SOPs, and data standards that ensure reproducible, publication-quality results across the cardiac biology platform

ML & Cross-functional Partnership

• Experiment Design for ML:
  Partner with ML and computational biology teams to design experiments that generate high-quality training data and to interpret model outputs in biological context
• Target Identification:
  Contribute to target identification and validation efforts by connecting cellular phenotypes to human genetic evidence

Scientific Leadership

• Mentorship:
  Mentor junior scientists and technicians, and contribute to a collaborative, scientifically rigorous team culture

Experience & Qualifications

• Advanced Degree:
  Ph.D. in cell biology, biomedical engineering, cardiac physiology, or a related field, with at least 3 years of industry experience
• iPSC-CM Expertise:
  Deep hands-on experience in iPSC culture, cardiac directed differentiation, and iPSC-CM characterization across both 2D and 3D/EHT platforms
• Genetic Disease Modeling:
  Track record building and applying genetic iPSC-CM disease models — patient-derived lines, CRISPR-edited isogenic pairs — to study cardiovascular mechanisms
• Cardiovascular Biology:
  Strong foundational knowledge of cardiomyocyte biology and cardiovascular disease
• High-Content Imaging:
  Experience with high-content imaging and/or automated microscopy platforms, including image analysis pipelines
• Human Genetics:
  Familiarity with GWAS and human genetic evidence, and experience using these data to prioritize disease mechanisms or therapeutic targets
• Scientific Communication:
  Track record of executing rigorous…