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.
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.
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.
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.
Location: Lausanne, Switzerland (on-site)
Type: Full time
Start date: ASAP
Application deadline
We are reviewing applicants on a rolling basis.
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Adaptyv is a company focused on developing and improving automated data processing pipelines for biophysical platforms. They define data quality standards and identify requirements for enhancing data review workflows.