ML Researcher – Molecular Privacy

At Apheris, we power federated data networks in life sciences to address the data bottleneck in training highly performant machine learning models. Publicly available molecular datasets are insufficient to train models that meet real industry requirements. Our product enables biopharma organizations to collaboratively train higher-quality models on their combined data, while ensuring that data ownership, IP, and governance remain with the original custodians. We are looking for a Senior ML Engineer to take technical ownership of privacy risk assessment & mitigations within our federated modelling initiatives.

Requirements

• 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.

Benefits

• Industry-competitive compensation, incl. 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
• Regular team lunches and social events
• Generous holiday allowance
• Quarterly All Hands meet-up at our Berlin HQ or a different European location