Technical Lead, Apheris

• You have a PhD, MSc, or equivalent experience in a relevant field, plus 5+ years applying ML to complex scientific or biological problems, ideally instructural biology, antibody engineering, biologics discovery, developability prediction, binderpredictionorprotein design
• You have hands-on experience with modern ML systems in Python andPyTorch, and have worked with or extended large-scale models such asOpenFold, AlphaFold, Boltz, ESM, or similar
• You haveMLOpsor ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows
• You can define success criteria,validatemodel quality, and ensure ML releases are robust enough for real-world use
• You have led delivery of complex ML projects, including setting technical direction, managing risks and dependencies, and driving teams toward high-quality releases
• You are comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly tomodeling, experimentation, or architecture when needed
• You can work effectively with product, research, leadership, customers, and scientific stakeholders to turn ambiguous requirements into clear technical plans

Desirable

• 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
• You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows
• You will set technical direction, drive execution, challenge modeling decisions, and turn ambiguity into executable plans, while managing risks and dependencies, mentoring senior engineers and ML scientists, and getting into technical depth when needed
• We are looking for someone who has led demanding ML delivery before and knows how to move from research-led or open-source prototypes to robust model systems
• Lead teams building and delivering federated large molecule AI systems, staying hands-onacross antibodymodeling, co-folding, binder prediction, and developability
• Build and implement ML applicationslarge biomolecular foundation modelssuch asOpenFold, 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.Guide evaluation decisionsand build on them to deliverresultspackages to external stakeholders
• Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations
• Align consortium members onobjectives, evaluation criteria, data requirements, timelines, and delivery expectations
• Work with product, engineering, research, and leadership to ensure application requirements shape the model roadmap