Public summary
We are hiring a Technical Lead to manage and deliver advanced AI systems focused on large molecule modeling for pharmaceutical R&D. The role involves leading teams to develop and operationalize large-scale machine learning models for antibody modeling, co-folding, and biologics discovery, integrating complex scientific goals into reliable, scalable solutions used in drug discovery workflows. This hands-on leadership position requires expertise in modern ML frameworks, federated learning, and model deployment in regulated environments. Candidates should have extensive experience in computational biology or related fields and proven leadership in delivering complex ML projects.
Location and work setup
- Location
- Berlin
- Remote status
- Unknown
- German requirement signal
- No German Required Detected
- Detected job language
- English
Responsibilities
Lead and deliver federated large molecule AI systems focusing on antibody modeling, co-folding, and developability prediction. Set technical direction, translate scientific goals into clear plans, manage risks and dependencies, mentor senior engineers and ML scientists, and ensure timely release of robust model systems. Collaborate with product, engineering, research, and leadership to align objectives and ensure application requirements influence the model roadmap.
Qualifications
PhD, MSc, or equivalent experience with 5+ years applying machine learning to scientific or biological problems, ideally in structural biology, protein engineering, or related fields. Hands-on experience with Python and PyTorch, familiarity with large-scale models such as OpenFold, AlphaFold, Boltz, or ESM. Experience in MLOps, Kubernetes-based model training and deployment, defining success criteria, validating model quality, and leading complex ML delivery projects. Ability to mentor teams and work effectively across multidisciplinary stakeholders.