Using the immune system to cure disease. Backed by Y Combinator.
About the role
As Principal Scientist, you will lead the science at our San Francisco site. You will manage our team of scientists and research associates, set experimental strategy, and own the scientific decisions that determine where we invest our experimental effort. You will remain personally at the bench on the hardest problems.
What they're looking for
- A deeply experienced experimental scientist. You have spent your career developing complex biological assays and understand how difficult it is to make living systems behave reproducibly
- A rigorous troubleshooter. When data are ambiguous, you design experiments that distinguish competing explanations rather than relying on intuition alone
- A systems thinker. You understand that biological performance emerges from the interaction of cells, reagents, protocols, operators, instruments, automation, software, and data systems
- Hands-on. You are comfortable setting scientific strategy and equally comfortable going to the bench when a difficult problem requires it
- Comfortable with ambiguity. You can bring structure to immature technologies without imposing unnecessary bureaucracy or pretending uncertainty does not exist
- Mission driven. You are excited by the opportunity to replace animal models with scalable human biology and fundamentally change how medicines are discovered
More about this role
Drug discovery has a translation problem: more than 95% of drugs that succeed in animal models fail in humans. Parallel Bio is building an alternative: human-first drug discovery powered by organoids, automation, and AI, running on real human biology from the very first experiment.
Our platform has demonstrated strong concordance with clinical patient data and the ability to model complex human biology, including immunotoxicology, immune stimulation, and autoimmune disease. Pharmaceutical partners, including Fortune 500 companies, are already using the platform, and we have raised approximately $30M from investors including AIX Ventures, Marc Benioff, Jeff Dean, and Y Combinator.
The next challenge is scale and depth.
Complex human biology does not become a platform because it works once at the bench. Our models have to be understood deeply enough that we know how they behave across donors, conditions, and operators, and can stand behind what they tell us in front of a pharmaceutical partner. That understanding is what makes characterization, optimization, transfer, automation, and scale possible, and it is scientific work before it is procedural work.
We are looking for a...
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