This Principal Scientist role leads the science at the San Francisco site, managing a team of scientists and research associates, setting experimental strategy, and owning scientific decisions. The role involves hands-on work on challenging problems, focusing on understanding organoid model behavior, building reference data, and ensuring rigor in workflows. It also includes assay development, technology transfer from R&D, and serving as the scientific lead for external partner programs.
Responsibilities
Lead the scientific team at the San Francisco site, including hiring, managing, and developing scientists, and maintaining high standards for experimental rigor and pace
Set scientific direction and experimental strategy for the platform, and bring competing interpretations to a clear conclusion
Run regular data review with the team, distinguishing biological effects from technical or process effects and driving toward experiments that resolve questions
Establish high standards for experimental design, controls, documentation, reproducibility, and scientific decision-making, and hold the team to them
Facilitate productive scientific disagreement and reach clear conclusions quickly, without suppressing dissent or letting debate stall the work
Serve as the scientific lead for external partner programs, from experimental design through data delivery
Remain close to the bench and personally engage in difficult experimental and troubleshooting work
Build biological systems from first principles, taking models from concept to working, characterized assays, and deepening the ones already running
Own the development and maturation of biological assays and workflows intended for execution on the Parallel Bio platform
Translate early R&D assays into robust, repeatable, scalable experimental processes
Develop and execute characterization and optimization strategies for complex organoid and cell-based assays
Identify critical assay and process parameters and understand how they influence biological outputs
Design experiments that establish appropriate operating ranges, robustness, and sources of variability
Lead troubleshooting of complex biological and process failures using structured, hypothesis-driven experimentation
Define and build the framework for transferring assays and biological processes from R&D into development and ultimately routine Technical Operations
Define clear entry and exit criteria for each stage of assay maturation and technology transfer
Establish objective acceptance criteria for platform readiness, including controls, expected ranges, reproducibility requirements, and system suitability
Ensure transferred assays have sufficient documentation, reference data, controls, and scientific understanding to support reliable execution
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Partner with Technical Operations to ensure new processes can be executed reproducibly by trained operators rather than only by their original developers
Lead or support comparability studies when assays, instruments, workflows, reagents, software, or biological inputs change
Develop strategies for scaling biological workflows from small exploratory experiments into higher-throughput platform processes
Understand and reduce variability introduced by operators, instruments, automation, reagents, donors, biological materials, and environmental conditions
Establish appropriate process controls and monitoring strategies for complex biological workflows
Partner with automation and software teams to translate manual scientific workflows into automated, observable, and reproducible platform processes
Identify where additional automation, instrumentation, software, or process controls will materially improve scientific performance
Build processes that balance scientific rigor with throughput, cost, cycle time, and operational complexity
Partner closely with R&D to ensure new assays are designed with eventual scale and platform implementation in mind and are ready to tech transfer
Partner with Technical Operations to establish reliable processes for external project execution
Work with Platform Software, automation, hardware, and data teams to ensure biological requirements are translated into effective platform capabilities
Contribute to platform roadmap decisions by identifying scientific and process-development capabilities required for future programs
Support external pharmaceutical programs by ensuring platform processes are scientifically defensible, reproducible, and appropriate for the questions being asked
Requirements
Ph.D. in Immunology, Cell Biology, Biology, Bioengineering, Biomedical Engineering, Molecular Biology, or a related discipline
Typically 10 to 15 or more years of relevant experience developing complex biological assays, models, and platforms
Demonstrated significant experience in several of the following areas:
Deep immunology expertise and hands-on work with complex human in vitro models, organoids, primary immune cells, or advanced mammalian cell culture
Direct management of bench scientists, including setting scientific direction, running data review, and developing scientific judgment in a team
Building biological systems from 0 to 1 and adapting them onto automated or higher-throughput platforms
Experience building Process Development functions or technology-transfer frameworks in an early-stage biotechnology company
Assay development, characterization, optimization, and technology transfer
Identifying and controlling biological and technical sources of variability
Developing assays or processes for automated or high-throughput environments
Translating early-stage R&D methods into robust workflows used by other scientists or operational teams
Establishing controls, acceptance criteria, reference standards, or system-suitability approaches for biological assays
Leading technically complex investigations and experimental troubleshooting
Nice to have:
Significant experience developing organoids, immune-cell assays, advanced human cell models, or other highly variable biological systems
Experience with Design of Experiments, process characterization, capability analysis, or Quality by Design approaches
Experience working in regulated, quality-controlled, or highly documented scientific environments
Experience supporting pharmaceutical or biotechnology partners and translating external scientific questions into executable platform workflows
Experience partnering deeply with robotics, automation, software, data science, or machine-learning teams
Important Details
Day one: Meet the scientific, Process Development, Technical Operations, automation, software, and platform teams. Immerse yourself in organoid models, assays, workflows, instrumentation, data, and current external programs
Week one: Go deep on models, assays, protocols, instrumentation, and data. Form your own view of how the models behave, where the reference data and controls are strongest, and where additional scientific work will most improve reliability for external execution
Month one: Set acceptance criteria for core organoid workflows and build out the reference data and controls that support them. Stand up a recurring scientific review forum with the San Francisco team. Align R&D, development, and Technical Operations on ownership and decision rights, and begin driving focused characterization experiments against the highest-priority gaps
About the Company
Parallel Bio is building human-first drug discovery, powered by organoids and AI, running on real human biology from the very first experiment. Its platform is 87% concordant with clinical patient data, a vast improvement over the 3% translational success rate of animals. The company has demonstrated the ability to model immunotoxicology, immunogen stimulation, and two autoimmune diseases, with more on the way. Numerous pharma partners, including 3 Fortune 500 companies, are already using the platform. Parallel Bio has raised ~$30M from AIX Ventures, Marc Benioff, Jeff Dean, and Y Combinator. The company aims to build the first scaled engine for generating real human biological data and use it to fundamentally change how medicines are discovered.
Parallel Bio created a human immune system in a dish to discover drugs and immunotherapies more likely to work in patients..
