Key skills
KubernetesPythonPyTorchMLMLOpsMentoring
About this role
Role Overview
- Lead teams building and delivering federated co-folding models
- Build and implement ML applications in structural biology
- Own delivery against committed milestones ensuring high-quality model releases
- Translate ambiguous scientific and technical goals into clear plans, priorities, and decisions
- Guide evaluation decisions and build deliverables for external stakeholders
- Surface risks, blockers, and technical trade-offs with clear recommendations
- Align consortium members on objectives, evaluation criteria, and delivery expectations
Requirements
- PhD, MSc, or equivalent experience in a relevant field
- 5+ years applying ML to complex scientific or biological problems
- Hands-on experience with modern ML systems in Python and PyTorch
- Experience with or extended large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar
- MLOps or ML infrastructure experience with Kubernetes-based workflows
- Ability to define success criteria, validate model quality, and ensure robust ML releases
- Experience leading delivery of complex ML projects
- Comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly
Tech Stack
- 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)