Director of ML Research – AI Applications

About the role

We are building a new ML Research team within the broader AI Applications group at Apheris. As the founding leader of the team, you will define its direction, build and mentor a high-performing group of researchers and engineers over time, and work directly on some of the most strategically important modelling questions across our structural biology and ADMET initiatives. This is a player-coach role focused on applied research: taking strong ideas from the literature and adapting them to high-value biological and customer problems.

What you'll do

• Set up and lead the dedicated ML Research team within AI Applications, working alongside existing engineering teams and establishing the research mandate for the organisation.
• Design, enhance, and train foundation models at scale for structural biology and co-folding, addressing core challenges in protein interaction modelling and drug discovery.
• Leverage large-scale proprietary structural biology and biophysical datasets to develop improved data pipelines and model architectures that capture geometric and physical priors.
• Translate advances in structural biology ML and adjacent literature into practical modelling approaches for real-world drug discovery problems.
• Lead cross-functional delivery across AISB, ADMET, engineering, product, and privacy teams, ensuring research outputs integrate into production workflows.
• Collaborate with academic partners on co-folding and structural biology research, contributing to publications and presenting findings at leading conferences.
• Represent Apheris in customer discussions and scientific forums, and help solve high-impact modelling problems across multiple pharma partners.

What you'll need

• Postgraduate degree (PhD or MSc) in Computer Science, Machine Learning, Computational Biology, or a related field, with 7+ years of relevant experience including 3+ years in technical leadership.
• Strong experience applying machine learning to biological problems, particularly in structural biology (e.g. cofolding, protein modelling) or adjacent domains such as ADMET.
• Proven publication track record in top-tier ML or computational biology venues (e.g. NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar).
• Hands-on experience with modern ML systems (Python, PyTorch) and with large-scale models (e.g. OpenFold, Boltz, or similar).
• Comfortable operating as a player-coach: setting technical direction, leading teams, and contributing directly to modelling and experimentation.
• Effective in cross-functional and customer-facing environments and can translate ambiguous scientific problems into clear technical approaches.

Nice to have

• Experience in early-stage biotech or building ML systems or research functions from scratch.
• Experience training large models with distributed training across GPU clusters or cloud platforms such as AWS, Azure, or Lambda.
• Strong ML Ops and machine learning infrastructure experience, particularly with Kubernetes-based workflows.
• Experience developing QSAR models with classical machine learning or deep learning methods.
• Experience in federated learning, privacy-preserving ML, or multi-party training environments.

What they offer

• Industry-competitive compensation, including early-stage virtual share options
• Remote-first working
• Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget
• Generous holiday allowance
• Office Days at Berlin HQ or different European location (3x per year)