Scientist, Protein Expression & Purification

Adaptyv is building an automated lab that lets AI agents run biology experiments. We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results. But they can't run the experiments themselves - that's still a manual, months-long process. We're building the infrastructure that gives AI agents access to the physical world. We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward. This is a rare chance to help advance some of the most important work happening in biotech today. Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API-controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical-world data, and AI systems that troubleshoot production results and accelerate assay development. We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation. ABOUT THE ROLE You'll build out the data science layer of Adaptyv's foundry — the work that turns tens of thousands of raw, messy experimental readouts into clean, trustworthy, structured data that our customers, our models, and our own scientists can rely on. Binding (BLI/SPR), developability, biophysical, and functional assays all produce data at scale; your job is to make that data correct, comparable, and useful. This sits at the intersection of three things: data quality (is this number real, or an artifact?), bioinformatics (linking experimental results back to sequence, structure, and protein design), and dataset building (turning foundry output into the kind of high-quality, benchmarkable data that frontier AI labs actually want). You'll work shoulder-to-shoulder with the lab scientists who run the assays, the software team who own the pipelines, and the customers who train models on what we produce. This is a hands-on build role, not a management one. WHAT YOU'LL DO * Own the scientific logic of data quality across the foundry: define what "good data" looks like for each assay type — expected signal ranges, control thresholds, failure modes, edge cases — and turn it into automated checks. * Build anomaly detection and QC models that catch bad data the eye would miss: assay drift, instrument variability, plate effects, false passes and false fails — and distinguish real signal from noise statistically. * Work with the software and ML teams to specify, review, and improve the automated data pipelines that process instrument outputs, feeding back precise requirements for what to flag, auto-reject, or route to human review. * Connect experimental results back to the protein side — sequence, structure, and design — so wet-lab data and computational models reinforce each other. * Turn foundry output into structured, documented, benchmark-grade datasets that are a genuine asset for our customers and for training and evaluating protein-design models. * Apply real statistical rigor to multi-condition data at scale — thousands of samples across hundreds of simultaneous experiments — and make the results interpretable and comparable across runs. WHAT WE'RE LOOKING FOR * Strong data science / bioinformatics background — you're fluent in Python (pandas, numpy, the scientific stack) and comfortable owning messy, real-world experimental data end to end. * Genuine biology grounding — you understand proteins, assays, and sequence/structure/function well enough to know what the data means, not just how to process it. You don't need to be a bench scientist, but you can't be biology-blind. * Statistical maturity — process control, anomaly detection, handling variability and batch effects; you can tell drift from noise and defend the call. * Prolific builder with the receipts to prove it. You've shipped a lot — pipelines, tools, models, datasets — and can point to concrete things you built end to end and put into real use, not prototypes that died in a notebook. You move fast, systematize what works, and have no patience for babysitting a fixed dashboard. * AI-native builder. It's 2026 — you build with coding agents like Claude Code as a default, and you have sharp judgment about what they produce. * Interdisciplinary by instinct. You're energized working across the lab bench, software, and ML, and you treat automation and data infrastructure as part of your job. * Bonus: experience with protein/sequence-structure data (bioinformatics tooling, structural data), ML on experimental data, or building datasets for model training and benchmarking. DETAILS * Location: Lausanne, Switzerland (on-site) * Type: Full time * Start date: ASAP Application deadline We are reviewing applicants on a rolling basis.

Adaptyv is building an automated lab that lets AI agents run biology experiments. We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results. But they can't run the experiments themselves - that's still a manual, months-long process. We're building the infrastructure that gives AI agents access to the physical world. We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward. This is a rare chance to help advance some of the most important work happening in biotech today. Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API-controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical-world data, and AI systems that troubleshoot production results and accelerate assay development. We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation. ABOUT THE ROLE You'll build out antibody developability at Adaptyv — the panel of assays that separates a nice binder from a manufacturable, stable, well-behaved therapeutic. Aggregation, thermostability, self-association, polyreactivity, solubility, viscosity, and chemical liabilities: you'll build the experimental stack that flags these early, and make it something customers can order as a product. These assays exist today as a scattered, bespoke collection. Your job is to turn them into one coherent, automated, high-throughput developability assessment — and to connect the experimental data to in-silico prediction so the lab and the models reinforce each other. You'll develop the assays hands-on, work with lab automation to scale them, and work with the software and ML teams to model the data and make it useful for protein designers. You'll lead the science and stay at the bench. WHAT YOU'LL DO * Build and validate a developability assessment panel hands-on: thermostability (DSF/nanoDSF), aggregation and self-association (SEC, HIC, AC-SINS, DLS), polyreactivity/nonspecificity, solubility, and chemical-liability assessment. * Turn that panel into a productized, high-throughput service customers can order against their antibody campaigns. * Partner with lab automation to scale and automate the assays for reproducible, fast turnaround. * Work with the software and ML teams to structure developability data and connect it to computational developability prediction. * Interpret results and advise customers on liabilities, risk, and what to engineer next. * Set the scientific standard for what developability data we trust and report. WHAT WE'RE LOOKING FOR * Deep, hands-on experience assessing antibody developability — you know the assay panel cold and have used it to triage real antibody campaigns. * Applied industry experience. You've done this in an antibody discovery, engineering, or development setting where decisions and timelines were on the line — not primarily in an academic research project. This is the core requirement. * Strong grasp of what makes a biologic developable — the link between early biophysical signals and downstream manufacturability and stability. * Builder who moves fast. You want to stand up new capabilities and productize them, not run a fixed service. * Interdisciplinary. You collaborate naturally with automation engineers and with software/ML people, and you're excited about closing the loop between wet-lab data and predictive models. * Data fluency (scripting, working with structured datasets) is a strong plus. DETAILS * Location: Lausanne, Switzerland (on-site) * Type: Full time * Start date: ASAP Application deadline We are reviewing applicants on a rolling basis.