Adaptyv is building an automated lab thats let 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. Today, over 50 companies are already running their wet lab experiments on Adaptyv, ranging from some of the biggest biopharmas, to frontier AI labs to dozens of techbio startups. 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.

Responsibilities

Run and benchmark open-source protein design methods on real Adaptyv targets, validated against experimental data from our wet lab. Design binders for internal R&D campaigns, and track experimental performance across success rate, hit rate, kinetics, and developability. Build computational tooling around the design pipeline: structure prediction, filtering, and ranking, to triage large design pools down to candidates for synthesis and characterization. Support the technical side of our open competitions and hackathons: drafting track briefs, supporting participants, judging submissions, and writing up results. Contribute open data, blog posts, designer spotlights, and method comparisons to Proteinbase.

Qualifications

Currently enrolled in a Master's, PhD, or final-year undergraduate programme in computational biology, machine learning, biochemistry, biophysics, bioengineering, or a related field. Hands-on experience with at least one modern open-source protein design method. Strong Python and PyTorch skills. Comfortable working with biology data formats. A working understanding of binding kinetics and developability that extends beyond in silico metrics, with a healthy scepticism of computational scores as ground truth. Bonus: prior open-source contributions to a protein design repository, a strong showing in a public design competition, or a published blog post or paper. Duration: 3-6 months. Paid. Location: Remote, or on-site in Lausanne. Lausanne is preferred if you want lab access and the full design-build-test-learn exposure. Application deadline We are reviewing applicants on a rolling basis. Please include a link to a GitHub repository, competition submission, blog post, or other concrete work demonstrating your approach to protein design. Application deadline We are reviewing applicants on a rolling basis.