Intern, Protein Design

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.

Details

• 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

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.