At Parallel Bio, we are leveraging the human immune system to unlock safer, more effective drugs. We believe immunotherapies are the future of medicine, but their discovery is hindered by outdated models that fail to capture the complexity of the human immune system. Our platform overcomes these challenges by combining best-in-class human immune organoids with massive scale and advanced computational methods, including AI and machine learning. This allows us to generate unprecedented, population-scale insights into human health and disease. We can rapidly discover new drugs that we know will work in patients from the start and understand how they will perform across an entire population—something not possible with today's technology. This knowledge will allow us to engineer therapies that will work for as many people as possible, ensuring a safe and effective cure for everyone. We are a fast-paced, venture-backed company at a pivotal moment of growth. Join us on our journey as we create new tools to push the boundaries of what is possible. Role Overview You will design and qualify the immunological assays that determine what our platform can see. The questions we can answer about human immune biology across hundreds of donors, across perturbation conditions, and across disease contexts are bounded by how well our measurements capture what matters and exclude what doesn't. At population scale, getting this right is both high-leverage and difficult: systematic biases that would be invisible in a single study compound across hundreds of donors and propagate into every downstream model and decision. We are looking for a scientist whose primary orientation is the integrity of biological measurement. Someone who thinks about measurement as a system with failure modes, not as a set of protocols to execute. You will design and qualify the immunological assays that determine what our platform can see. The questions we can answer about human immune biology - across hundreds of donors, across perturbation conditions, across disease contexts - are bounded by how well our measurements capture what matters and exclude what doesn't. You will work at the interface of bench measurement and computational analysis, ensuring that our analytical systems are not only rigorous but designed to ask the right questions of the biology. Who You Are You think about measurement as a biological problem, not a procedural one. You understand that assay design interacts with the biology being measured, and that the difference between a decision-grade readout and an exploratory one is determined before any data is collected. You have a track record of catching problems others missed. You have a story about an assay or measurement you or your team trusted that turned out to be systematically wrong, and it changed how you work. You are quantitative and comfortable with data. You explore your own datasets, write code when it helps, and build intuition from data directly rather than waiting for someone else to analyze it. You do not need to be a computational biologist, but you gravitate toward quantitative reasoning. You understand enough immunology to know what you are measuring and why it matters. You can distinguish a biologically meaningful signal from a measurement artifact, not just statistically, but because you understand the immune biology in the tissue context you are studying. You are energized by being part of an interdependent team. You thrive when your work is tightly coupled with others, when you can see how what you do shapes what the team can do. As the function grows, you look forward to mentoring junior scientists and building something beyond your own contributions.
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Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
Number of Employees
1-10 employees