Scientist II, Arrayed Screening

About the role

Insitro is building a transformative drug discovery organization defined by the integration of multi-modal data and artificial intelligence. This Scientist II, Arrayed Screening role is central to generating the high-quality, decision-grade biological evidence that powers our machine learning models and therapeutic strategies. You will own the end-to-end development and execution of complex arrayed screening experiments, translating biological questions into robust, scalable protocols. This position sits at the dynamic intersection of cell biology, assay innovation, automation, and data science, requiring deep scientific judgment and meticulous execution. You will be a key contributor to the experimental engine that drives target validation and candidate selection across therapeutic programs. Success in this role requires intellectual curiosity, strong technical ownership, and the ability to work seamlessly within a highly collaborative and fast-paced environment.

Key facts

What you'll do

Design and execute sophisticated arrayed screening assays in 96- and 384-well formats, incorporating diverse readouts such as endpoint measurements, kinetic analyses, high-content imaging, and biochemical detection methods.

Develop and validate novel cell-based screening workflows utilizing a broad spectrum of cellular models, including immortalized lines, primary human cells, iPSC-derived derivatives, and sophisticated engineered cellular systems.

Lead efforts in small-molecule, genetic, and multiplexed perturbational screening campaigns, driving target validation, mechanism-of-action exploration, and rigorous screening quality control.

Collaborate closely with automation and process engineering teams to successfully translate assays onto high-throughput liquid handling platforms, optimizing protocols for systems such as Echo, Hamilton, Bravo, and Tecan.

Implement sophisticated data analysis pipelines using statistical methods and visualization tools to identify meaningful biological trends, diagnose assay anomalies, and distill complex results into clear, actionable narratives.

Partner with machine learning and data science functions to curate, annotate, and structure high-dimensional screening outputs, ensuring datasets are reproducible, well-curated, and optimal for model consumption.

Translate dense biological questions into precise experimental designs by working cross-functionally with disease biology, genetics, cell modeling, and drug discovery research teams.

Champion rigorous data management and documentation standards within Benchling or LIMS environments, actively contributing to the establishment of best practices around assay robustness, reproducibility, and data integrity.

Effectively communicate experimental strategies, interim findings, and strategic recommendations to multidisciplinary project teams through clear written and verbal exchanges.

Continuously evaluate emerging technologies and methodologies to enhance the throughput, quality, and scientific depth of arrayed screening capabilities.

Requirements

Possess a PhD in cell biology, molecular biology, biochemistry, pharmacology, bioengineering, chemical biology, or a closely related quantitative life science discipline.

Bring a minimum of 2-5 years of relevant post-PhD or equivalent industry experience, demonstrating a proven track record of independent assay design, project leadership, and scientific problem-solving.

Showcase strong hands-on expertise in developing, optimizing, and executing cell-based assays specifically within high-throughput plate formats.

Demonstrate deep proficiency with high-content imaging assays, fluorescence and luminescence-based readouts, cell viability measurements, reporter gene assays, immunostaining protocols, and flow cytometry analysis.

Maintain strong mammalian cell culture competencies, including strict aseptic technique and the ability to work confidently with immortalized, primary, stem-cell-derived, and other engineered cellular models.

Possess substantial experience with molecular biology techniques, such as cloning, CRISPR/gene editing, transgene expression, stable cell line generation, and quantitative PCR methods.

Have direct experience with screening automation platforms and liquid handling technologies, including the operation and optimization of systems like Echo, Hamilton, Bravo, and Tecan.

Be thoroughly comfortable working with small molecule libraries, biologics, siRNA/ASO reagents, CRISPR screening tools, and other cellular perturbation modalities.

Exhibit a strong capacity to analyze, interpret, and clearly communicate complex, multi-dimensional biological datasets to diverse audiences.

Leverage analytical tools such as GraphPad Prism, Spotfire, Benchling, Python, or R, and demonstrate a keen interest in the application of agents and LLM technologies to enhance scientific workflows.

Display exceptional organizational skills, meticulous attention to detail, and the capacity to effectively manage multiple concurrent experiments or project workstreams under tight deadlines.

Embody a strong collaborative mindset and genuine enthusiasm for operating within a multidisciplinary environment that fuses biology, engineering, computation, and data science.

Nice to have

Hold prior professional experience in high-throughput screening within biotechnology or pharmaceutical research settings.

Have a track record of developing phenotypic assays using relevant human disease models.

Have experience constructing and analyzing datasets specifically for machine learning applications, including image-based analytics and quantitative modeling.

Demonstrate expertise in assay miniaturization, troubleshooting on automated platforms, application of Z-factor metrics, rigorous dose-response analysis, and comprehensive screening data quality control.

Possess experience working across therapeutic modalities and disease areas, contributing to target-specific screening strategies.

Practical notes

This role requires onsite presence at our South San Francisco office 5 days per week.