Technical Lead - Structural Biology Networks

About the role

Technical lead to own delivery of AI Structural Biology model programs. This is a hands-on leadership role at the intersection of foundation models, structural biology, and federated learning. You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows.

What you'll do

• Lead the teams building and delivering federated co-folding models, staying hands-on across modeling, architecture, evaluation, and engineering execution.
• Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold.
• 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, protein modeling, co-folding, or binding prediction.
• 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.
• Can define success criteria, validate model quality, and ensure ML releases are robust enough for real-world use.
• Have led 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.
• Can work effectively with product, research, leadership, customers, and scientific stakeholders to turn ambiguous requirements into clear technical plans.

Nice to have

• Experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments.
• Experience with Go or other systems programming languages.
• Worked on 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 European location (3x per year)