Machine Learning Engineer

Who you are

• You should bring practical experience working with contemporary models for protein structure and molecular complex prediction, and be comfortable moving from research-led or open-source prototypes to production-grade model systems
• You have strong hands-on experience building and training modern ML systems in Python and PyTorch
• You have worked with protein structure, cofolding or related structural-biology models such as Boltz, OpenFold, AlphaFold-derived or similar systems
• You understand structural-biology data and can design sound preprocessing, training and evaluation workflows
• You can take a research or open-source prototype through to a reliable model system used by scientists or customers
• You can own ambiguous technical problems and communicate effectively with scientific, product and engineering stakeholders
• Experience with federated learning, privacy-preserving ML or distributed training
• Experience delivering ML systems in pharmaceutical, biotech or other high-trust environments
• Relevant publications or open-source contributions

What the job involves

• We're looking for a Machine Learning Engineer to help drive the technical execution of our cofolding and structural-biology model programs
• This is a hands-on role at the intersection of foundation models, structural biology, and federated learning
• You'll work closely with our technical lead and leadership team, turning ambitious scientific goals into model systems that can be evaluated, released, and used in real drug-discovery workflows
• Fine-tune and extend foundation models for biomolecular cofolding, complex structure prediction and related drug-discovery tasks
• Own the applied modelling lifecycle, from data preparation and experimental design through benchmarking, evaluation and delivery
• Work with customers and academic partners to translate scientific questions and proprietary data into concrete modelling projects
• Build reliable, scalable pipelines for model training, inference and deployment, including in federated or multi-party environments
• Collaborate across research, product and engineering, communicating model limitations, risks and technical trade-offs clearly

Benefits

• Early-stage virtual share options
• Remote-first working, work-from-home budget & co-working stipend
• Wellbeing budget & mental health benefits
• Learning & development budget
• Regular team lunches & social events
• Generous holiday allowance
• Office Days at our Berlin HQ or a different European location (3x a year)
• A fun, diverse team of mission-driven individuals with experience across leading organizations
• Plenty of room to grow personally and professionally & shape your own role