Scientist I/II, integrated Technology and Exploration (iTX)

THE OPPORTUNITY At insitro, AI, large-scale biology, and multi-modal target and drug discovery are not separate disciplines. They operate as one system. Our advantage comes from building an integrated loop where models generate predictions, experiments generate ground truth, and each cycle updates what the system knows. We are hiring a Scientist to generate the ground truth this system learns from. We are looking for someone grounded in cell biology who treats AI-generated predictions as hypotheses to be tested experimentally, and who is motivated by building systems that learn through contact with reality. You will design and run targeted cell-based experiments across manual and automated lab workflows to answer specific questions raised by the models and the programs they support. When experimental results and model predictions diverge, your responsibility is to determine why: whether the issue lies in the model, the experiment, or the experimental system, and to guide what the system learns next. Biology doesn’t respond to narratives, only evidence. Success in this role is measured not just by execution, but by whether the system becomes more accurate, better calibrated, and more useful as a result of your work. This role is based at our South San Francisco headquarters, five days a week, to support tight feedback loops between experiments, automation, and AI-driven analysis. The position reports to the Director of Integrated Technology Exploration.   RESPONSIBILITIES - Generate ground truth datasets from cell-based experiments that serve as training and validation data for computational models - Design and execute experiments to test and validate/invalidate AI-generated predictions, selecting cell models, perturbations, readouts, and timepoints based on what the system most needs to learn - Execute experimental work across manual bench workflows and automated platforms, including imaging-based phenotyping, perturbation screens, and multi-modal molecular readouts - Diagnose discrepancies between model predictions and experimental outcomes, determine their root cause, and articulate what the system should learn or change as a result - Maintain structured experimental records where quantitative claims are sourced, unexpected results are documented, and findings are accessible to both human collaborators and computational systems - Apply scientific judgment to decisions about when evidence is sufficient, when uncertainty remains too high, and when approaches should be revised or stopped - Collaborate with biologists, automation engineers, and machine learning and data scientists to translate experimental insights into model improvements and guide subsequent experimental questions   ABOUT YOU - You have a strong foundation in cell-based experimental biology, with a PhD in a biological science (e.g., cell biology, biochemistry, genetics, or related field) and 2+ years of industry experience, or an MS with equivalent depth of industry experience - You treat AI-generated predictions the same way you treat any hypothesis: worth considering, worth testing, never accepted without experimental evidence - You're comfortable working without a playbook. AI-native experimental science is a new discipline, and you're ready to help define what good practice looks like - You design experiments to be maximally informative, not maximally confirmatory. An experiment that cleanly rules something out is as valuable as one that validates it - You are comfortable designing and executing experiments in both manual and automated laboratory environments, and you see automation as a tool for learning rather than simply for throughput - You're analytically fluent with experimental data. You can work with imaging readouts, plate-level metadata, and gene-level results - You want to understand how models reason, where they fail, and how experiments can make them better - You work well independently and in multidisciplinary settings, bringing clarity, curiosity, and critical thinking to ambiguous problems NICE TO HAVE - Experience with high-content imaging or morphological profiling - Familiarity with CRISPR screening (pooled or arrayed formats) - Experience working with laboratory automation or LIMS   COMPENSATION & BENEFITS AT INSITRO Our target starting salary for successful US-based applicants for this role is $120,000 - $160,000. 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 wit