Director of ML Research – AI Applications

Role Overview

• 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.
• Build and mentor a high-performing team of ML researchers and engineers over time.

Requirements

• You hold a postgraduate degree (PhD or MSc) in Computer Science, Machine Learning, Computational Biology, or a related field, and have 7+ years of relevant experience, including 3+ years in technical leadership.
• You have strong experience applying machine learning to biological problems, particularly in structural biology (e.g. cofolding, protein modelling) or adjacent domains such as ADMET.
• You have a proven publication track record in top-tier ML or computational biology venues (e.g. NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar).
• You have hands-on experience with modern ML systems (Python, PyTorch) and have worked with or extended large-scale models (e.g. OpenFold, Boltz, or similar).
• You are comfortable operating as a player-coach: setting technical direction, leading teams, and contributing directly to modelling and experimentation.
• You are effective in cross-functional and customer-facing environments and can translate ambiguous scientific problems into clear technical approaches.
• Bonus points if you have experience in early-stage biotech or in building ML systems or research functions from scratch.
• You have experience training large models, including distributed training across GPU clusters or cloud platforms such as AWS, Azure, or Lambda.
• You have strong ML Ops and machine learning infrastructure experience, particularly with Kubernetes-based workflows.
• You have experience developing QSAR models with classical machine learning or deep learning methods.
• You have experience writing Triton kernels or otherwise optimising model performance at the systems level.
• You have experience in federated learning, privacy-preserving ML, or other multi-party training environments.

Tech Stack

• AWS
• Azure
• Cloud
• Kubernetes
• Python
• PyTorch

Benefits

• Industry-competitive compensation, including early-stage virtual share options
• Remote-first working – work where you work best
• Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget
• Generous holiday allowance
• Office Days at our Berlin HQ or a different European location (3x per year)
• A high-calibre, execution-focused team with experience from leading organizations