(Senior) ML Researcher – Molecular Privacy

About Apheris

Apheris powers federated life sciences data networks, addressing the critical challenge of
 accessing proprietary data locked in silos due to IP and privacy concerns. Publicly available
 datasets are insufficient to train high-quality ML models that meet industry requirements.
 Our product addresses this by enabling life sciences organizations to collaboratively train
 higher quality models on complementary data from multiple parties. We are now doubling
 down on two key areas of interest: structural biology and ADMET.

About the role

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. Our federated computing infrastructure is designed with privacy and control as first-class concerns.
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. This is a hands-on, high-impact role centred on understanding how real drug discovery models behave in practice, identifying where privacy risks emerge, and generating empirical evidence to assess and mitigate those risks.
You will work within our AI Applications Engineering team and act as a technical authority on privacy for machine learning in drug discovery. A key part of the role is mapping real-world model usage to concrete threat models and experimental designs and clearly communicating the resulting evidence and conclusions to external partners, consortium stakeholders, and internal leadership.
You should bring strong hands-on experience with machine learning models used in drug discovery—particularly structure-based and protein–ligand modelling, with exposure to adjacent areas such as ADMET. You should be comfortable working directly with modelling pipelines, uncertainty estimation, and model outputs to reason about privacy risk, rather than treating privacy as a theoretical or policy-driven concern.
If you want to be part of a mission-driven team building federated AI systems for life sciences, and you are motivated by turning complex modelling behaviour into clear, defensible privacy conclusions for high-stakes collaborations, this role is for you.

What you will 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.
• 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.

What we expect from you

By month 3:

• Develop a working understanding of Apheris’ product, federated training setup, and key life-sciences modelling use cases.
• Reproduce and extend at least one existing modelling pipeline to establish a baseline privacy and attack-surface assessment.
• Contribute to privacy analysis for one or more active federated drug discovery programs as they transition from setup into live operation.

 By month 6:

• Design and run practical privacy experiments on live federated modelling workflows (e.g. co-folding, binding, screening, ADMET), focusing on realistic leakage and attack scenarios.
• Generate quantitative and qualitative evidence and synthesize it into clear reports and slide decks for external and internal stakeholders.
• Act as a technically credible privacy counterpart in active program discussions across scientific, engineering, and governance audiences.

 By month 12:

• Own privacy risk assessment and