Intern, Protein Design

Embodied AI; Lausanne; Hybrid; CHF 17.000 - 28.000; Teilzeit

Zusammenfassung

Adaptyv in Lausanne is seeking talented students for a 3-6 month internship in computational biology and protein design. You will work on open-source design methods, bench-design targets, and help translate results into actionable insights.

You'll build tooling around the design pipeline, contribute to open data, blog posts, and participate in community challenges. Remote work is possible, with on-site access in Lausanne for lab exposure.

Qualifikationen

• 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 with biology data formats.
• Understanding of binding kinetics and developability beyond in silico metrics; skepticism of scores as ground truth.

Aufgaben

• Run and benchmark open-source protein design methods on Adaptyv targets against experimental data.
• Design binders for internal R&D campaigns and track performance metrics.
• Build computational tooling around the design pipeline: structure prediction, filtering, and ranking.
• Support open competitions and hackathons: briefs, participant support, submissions judging, and results write-up.
• Contribute open data, blog posts, designer spotlights, and method comparisons to Proteinbase.

Kenntnisse

Python

PyTorch

Open-source design methods

Biology data formats

Ausbildung

Master's/PhD or final-year undergrad

Jobbeschreibung

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.

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.