Forward-Deployed ML Engineer – Cofolding

As we are doubling down on structural biology use cases as a focus area within our drug discovery work, we are looking for a Senior ML Engineer to drive the technical execution for our structural biology models. This is a hands-on, high-impact role focused on advancing the state of the art in applying foundational models to structural biology problems. You'll work closely with our leadership team and will serve as the technical authority on ML modelling, architecture, and experimentation in this domain.

What you'll do

• Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold.
• Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines.
• Work with our customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data.
• Carry out case-studies associated with the above, providing scientific and technical expertise to our customers. You will be involved in the full project pipeline, from scoping through to results delivery and dissemination.
• Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production.
• Collaborate cross-functionally to ensure models address real-world drug discovery needs.

What you'll need

• Deep experience building and training contemporary models in production, at scale (e.g. AlphaFold, OpenFold, Boltz) and familiarity with modern MLOps tooling.
• Experience applying ML to real-world protein structure or drug discovery problems.
• Comfort working in a fast-paced startup environment and enjoying customer-driven projects.
• Understanding of the technical challenges of structural biology and ability to design scalable data preprocessing, training, and evaluation workflows.

Nice to have

• Experience in federated learning, privacy-preserving ML, or privacy-preserving model training.
• Publications in ML or biology journals/conferences (e.g., NeurIPS, ICML, Nature Methods, Bioinformatics).

What they offer

• Industry-competitive compensation, incl. early-stage virtual share options
• Remote-first working with flexibility for co-working spaces
• Wellbeing budget, mental health benefits, work-from-home budget, co-working stipend and learning and development budget
• Generous holiday allowance
• Quarterly All Hands meet-up at Berlin HQ or different European location