Jobs at Insitro

Build and own full-stack software for insitro’s laboratory platform, including React and TypeScript interfaces, Python services, database models, instrument and Benchling integrations, and agent runtimes. Partner with scientists, biology, automation, and machine learning teams to understand workflows and deliver useful, verifiable tools. Prototype agentic tooling, support production reliability, review code and designs, and contribute to accelerating drug development.

Leads a team of statistical geneticists developing machine-learning methods and reusable workflows for human genetics. Owns GWAS, rare-variant, and post-GWAS analyses to identify causal targets, and translates genetic findings into drug discovery programs. Sets technical strategy, mentors team members, partners with software engineers and computational biologists, and collaborates with therapeutic scientists. Requires deep statistical genetics expertise, people management, machine-learning fluency, publications, and a relevant PhD.

As the Director of Quality Assurance, you will lead the development of QA strategies and systems for AI-driven drug discovery, ensuring compliance with GXP standards, managing global vendors, and validating AI/ML models in clinical processes.

Conduct multimodal computational biology analyses across imaging, omics, and human cohort data to identify therapeutic targets, biomarkers, and disease mechanisms. Partner with experimental biologists on study design, assay optimization, and biological interpretation. Benchmark analytical workflows, apply statistical rigor, and communicate findings through reports, visualizations, presentations, and publications within cross-functional drug discovery teams.

Build and lead imaging software infrastructure spanning microscope acquisition, image processing, high-throughput ML pipelines, data visualization, quality control, and laboratory automation. Scale research prototypes into reliable systems processing millions of images daily while ensuring data integrity, lineage, reproducibility, and observability. Partner with laboratory, ML, and microscopy scientists to translate research needs into production software, own architectural decisions, document systems, and mentor engineers.

Counsel or Senior Counsel will draft and negotiate high-volume commercial, technology, research, vendor, supplier, and employment-related agreements. The role partners with R&D, lab, IT, procurement, finance, HR, CMC, and technical teams on MTAs, CRO/CDMO agreements, cloud and compute contracts, and AI/ML matters. Responsibilities also include improving templates, playbooks, approval workflows, CLM tools, and AI-enabled legal processes while providing practical business advice.

Build and lead insitro’s global regulatory affairs function across therapeutics, diagnostics, biomarkers, and multiple modalities. Develop regulatory strategies, direct IND, CTA, NDA, BLA, and MAA submissions, engage FDA, EMA, and other health authorities, manage regulatory risks, and shape emerging AI/ML regulatory policy. The role will establish systems, SOPs, and a regulatory leadership team while supporting clinical-stage programs through proof of concept and submission.

Develop and deploy computer-vision and ML pipelines for microscopy datasets, partner with biologists and engineers, benchmark and calibrate tools, extract disease-relevant phenotypes, and communicate results to cross-functional stakeholders.

Design, develop, and qualify automated lab workcells and liquid-handling protocols; integrate automation with LIMS and AI/ML pipelines; diagnose mechanical, software, and systems integration issues; collaborate with scientific, software, and systems engineering teams to translate workflows to automation; improve automation development, testing, and deployment; plan automation inventory and lifecycle. Onsite in South San Francisco.

The Senior Director of Project & Portfolio Management will lead cross-functional teams, develop integrated project plans, and establish AI-forward project management methodologies to advance drug candidates in a hybrid work environment.

Lead and grow a team of machine learning scientists to develop computer-vision methods for in‑vitro microscopy. Partner with lab and imaging software teams to co-develop assays, translate methods to computational biology, set roadmaps, participate in code and method reviews, and advance imaging QC and self-supervised/VLM-based approaches to drive therapeutic programs.