Technical Lead – Large Molecule AI Systems

About the role

We are looking for a technical lead to own delivery of our large molecule AI model programs. This is a hands-on leadership role at the intersection of foundation models, structural biology, protein engineering, and federated learning. You will lead teams building and operationalizing large-scale ML systems for antibody modeling, co-folding, developability prediction, and biologics discovery.

What you'll do

• Lead teams building and delivering federated large molecule AI systems, staying hands-on across antibody modeling, co-folding, binder prediction, and developability.
• Build and implement ML applications with large biomolecular foundation models such as OpenFold, Boltz-2 and ESM. Own delivery of these against committed milestones and ensure high-quality model releases ship on time.
• Translate ambiguous scientific and technical goals into clear plans, priorities, workstreams, and decisions.
• Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations.
• Align consortium members on objectives, evaluation criteria, data requirements, timelines, and delivery expectations.
• Work with product, engineering, research, and leadership to ensure application requirements shape the model roadmap.

What you'll need

• PhD, MSc, or equivalent experience in a relevant field, plus 5+ years applying ML to complex scientific or biological problems, ideally in structural biology, antibody engineering, biologics discovery, developability prediction, binder prediction or protein design.
• Hands-on experience with modern ML systems in Python and PyTorch, and have worked with or extended large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar.
• MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows.
• Ability to define success criteria, validate model quality, and ensure ML releases are robust enough for real-world use.
• Experience leading delivery of complex ML projects, including setting technical direction, managing risks and dependencies, and driving teams toward high-quality releases.
• Comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly to modeling, experimentation, or architecture when needed.

Nice to have

• Experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments.
• Experience with production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments.
• Publication record in top-tier ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar.

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)