ML Researcher – Molecular Privacy

About the role

As we double down on structural biology and ADMET as core areas within our drug discovery work, we are looking for a privacy-focused Senior ML Engineer to take technical ownership of privacy risk assessment & mitigations within our federated modelling initiatives. You will work within our AI Applications Engineering team and act as a technical authority on privacy for machine learning in drug discovery.

What you'll do

• Design and execute practical privacy risk experiments on real drug discovery models, mapping theoretical threats to realistic attack surfaces.
• Work hands-on with molecular and structural ML pipelines (e.g. protein–ligand models, co-folding architectures, ADMET / QSAR data) to identify how modelling choices, representations, and uncertainty exploration can expose sensitive signal.
• Build and adapt experimental tooling for privacy analysis, including uncertainty probing, generative reconstruction tests, and distributional leakage experiments.
• Generate technically credible privacy evidence through hands-on modelling and experimentation, and convert that evidence into clear, informative reports and presentations for consortium and customer decision-makers.
• Collaborate closely with ML engineers, scientific teams, and other privacy stakeholders to design mitigation strategies that are grounded in actual model behaviour and implementation constraints.

What you'll need

• Deep hands-on experience building and modifying machine learning models in drug discovery, particularly structure-based modelling and co-folding, with exposure to adjacent areas such as ADMET.
• Hands-on experience with privacy for machine learning and/or federated learning, including reasoning about privacy risk, model behaviour, and governance in distributed or multi-party settings.
• Ability to design empirical privacy experiments and draw defensible conclusions from quantitative and qualitative evidence.
• Ability to communicate complex technical risks clearly and credibly to senior scientific, technical, and leadership stakeholders.
• Comfort owning ambiguous, cross-cutting problems end to end and setting direction as well as executing.

Nice to have

• Published or led substantial technical work in machine learning or computational biology, with contributions in venues such as NeurIPS, ICML, ICLR, Nature Methods, Bioinformatics, or equivalent industry research outputs.
• Experience operating in industry consortia or multi-organization collaborations, and understanding the technical, political, and governance dynamics they impose.
• Experience helping define, defend, or standardize privacy or risk positions in customer-, partner-, or regulator-facing contexts.
• Experience acting as a technical authority, shaping standards, frameworks, or long-term direction rather than working solely against a predefined brief.

What they offer

• Industry-competitive compensation, including early-stage virtual share options
• Remote-first working with office days at Berlin HQ or European location (3x a year)
• Wellbeing budget, mental health benefits, work-from-home budget, co-working stipend, and learning and development budget
• Generous holiday allowance