Senior ML Researcher — Structural Biology & Drug Discovery

A senior research position leading structure-based work at the intersection of generative chemistry, structural biology, and physical simulation. The researcher owns the path from a chosen target protein to a small set of credible therapeutic candidates end-to-end, partnering closely with engineering. The role combines rigorous experimental design with a strong delivery bias: research that produces code other senior scientists can defend.

• Lead protein-structure modelling, ligand-protein interaction prediction, and binding-affinity studies for selected targets.
• Design, run, and ship virtual screening campaigns over large compound libraries with principled ranking and triage.
• Pair structural machine learning with physical simulation, including molecular dynamics and free-energy methods, when it improves outcomes.
• Convert research prototypes into pipelines that engineering can operate, with reviewer-friendly code.
• Communicate findings clearly to mixed technical and scientific audiences and engage with external collaborators.
• Mentor junior researchers and lift the experimental bar of the team.

• PhD in computational chemistry, biophysics, structural bioinformatics, computer science, or an adjacent machine learning field.
• Demonstrated work in modern structural or biomolecular ML, such as geometric deep learning, equivariant networks, protein-structure models, or generative chemistry.
• Strong Python and PyTorch (or JAX), with fluency in libraries like RDKit, OpenMM or GROMACS, Bio Python, and ESM- or Alpha Fold-class models.
• A track record of published work, preprints, or shipped scientific software.
• Mature software-engineering habits: version control, code review, testing, and reproducibility.
• Excellent written and spoken communication in English.

• Compute and tooling appropriate to the problem at hand.
• Conference, publication, and collaboration support.
• Flexible remote work across European and Americas time zones.