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.
Translate empirical findings into clear, technically credible privacy narratives for customers, internal stakeholders, and partner organizations.
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.

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.
Comfortable designing empirical privacy experiments and drawing defensible conclusions from quantitative and qualitative evidence.
Ability to communicate complex technical risks clearly and credibly to senior scientific, technical, and leadership stakeholders.
Comfort in owning ambiguous, cross-cutting problems end to end and setting direction as well as executing.

Industry-competitive compensation, including early-stage virtual share options
Remote-first working – work where you work best, whether from home or a co-working space near you
Great suite of benefits, including a wellbeing budget, mental health benefits, a work-from-home budget, a co-working stipend and a learning and development budget
Generous holiday allowance
Office Days at our Berlin HQ or a different European location (3x a year)
A fun, diverse team of mission-driven individuals with experience across leading organizations and a drive to see AI and ML used for good
High impact, significant ownership, and the opportunity to shape how Apheris scales its people and culture over the next phase of growth

Senior ML Researcher – Molecular Privacy

Key skills