Scientist II, Arrayed Screening

The Opportunity The unprecedented convergence of multi-modal data and AI has the potential to revolutionize drug discovery. insitro is harnessing this moment to redefine how medicines are developed for complex diseases. Our success depends on our ability to attract, develop, inspire, and retain amazing, diverse talent and build a collaborative, high-performing, inclusive, and equitable culture. insitro is seeking a highly motivated Scientist II, Arrayed Screening to join our experimental biology and screening organization. In this role, you will help build, optimize, and execute high-throughput arrayed screening workflows that generate high-quality, decision-grade biological datasets for drug discovery. This is a highly collaborative role at the interface of cell biology, assay development, automated screening, disease modeling, and machine learning. You will work closely with biologists, bioengineers, automation scientists, data scientists, machine learning scientists, and drug discovery teams to design and run scalable cell-based and biochemical assays across therapeutic programs. The ideal candidate brings deep hands-on experience in cell-based assay development, high-throughput screening, mammalian cell culture, and quantitative data analysis, and is excited to generate robust biological data that can power ML-driven drug discovery. This role reports to the Director, High Throughput Biology and requires you to be onsite at our South San Francisco office 5 days per week .

Responsibilities Develop, optimize, and execute arrayed screening assays in 96- and 384-well plate formats, including endpoint, kinetic, imaging-based, and/or biochemical readouts. Design and implement cell-based screening workflows using immortalized, primary, iPSC-derived, or engineered cellular models. Support small-molecule, genetic, and/or perturbational screening efforts, including assay development, target validation, mechanism-of-action studies, and screening campaigns. Partner with automation and process engineering teams to scale assays using liquid handlers and screening platforms such as Echo, Hamilton, Bravo, Tecan, or similar systems. Analyze screening and assay data using appropriate statistical and visualization tools; identify trends, troubleshoot assay performance, and communicate results clearly. Collaborate with ML and data science teams to ensure experiments generate reproducible, well-annotated, model-ready datasets. Work cross-functionally with disease biology, genetics, cell modeling, and drug discovery teams to translate biological questions into scalable experimental designs. Maintain rigorous documentation in Benchling/LIMS systems and contribute to best practices for assay robustness, reproducibility, and data quality. Present experimental plans, results, and recommendations to multidisciplinary project teams. About You PhD in cell biology, molecular biology, biochemistry, pharmacology, bioengineering, chemical biology, or related fields. 2–5 years of relevant post-PhD or equivalent industry experience, with demonstrated independence in assay design and project leadership. Strong hands-on experience developing and running cell-based assays in high-throughput plate formats. Experience with high-content imaging, fluorescence/luminescence assays, viability assays, reporter assays, immunostaining, flow cytometry, or other quantitative cellular readouts. Strong mammalian cell culture skills, including aseptic technique and experience with immortalized, primary, stem-cell-derived, or engineered cell systems. Familiarity with molecular biology approaches such as cloning, CRISPR/gene editing, transgene expression, stable cell line generation, qPCR, or related methods. Experience with screening automation and liquid handling platforms is strongly preferred. Comfortable working with small molecules, biologics, siRNA/ASO, CRISPR reagents, or other perturbation modalities. Ability to analyze, interpret, and clearly communicate complex biological datasets. Experience with GraphPad Prism, Spotfire, Benchling, Python, or R. Familiarity with agents and LLM’s is a plus. Excellent organizational skills, attention to detail, and ability to manage multiple experiments or project workstreams in parallel. Strong collaborative mindset and enthusiasm for working in a multidisciplinary environment that brings together biology, engineering, data science, and machine learning. What Will Make You Stand Out Prior experience in biotech or pharma high-throughput screening. Experience developing phenotypic assays for disease-relevant human cell models. Experience generating datasets for machine learning, image analysis, or quantitative modeling. Familiarity with assay miniaturization, automation troubleshooting, Z-factor / assay quality metrics, dose-response analysis, or screening data QC. Experience working across therapeutic areas such as neuroscience, metabolic disease, oncology, immunology, or rare disease.