Scientist II, Arrayed Screening

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.

• 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.

• 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.

• 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.

Our target starting salary for successful US-based applicants for this role is $136,000 - $145,000. 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 as well as mental health and well-being support

• Open, flexible vacation policy

• Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc)

• Quarterly budget for books and online courses for self-development

• New hire stipend for home office setup

• Monthly cell phone & internet stipend

• Access to free onsite baristas and daily lunch for employees who are either onsite or hybrid

• Access to a free commuter bus network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area

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, and if in doubt, contact us at [email protected].

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About 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.