ML Engineer – Large Molecules

• Build, fine-tune and extend large biomolecular models such as OpenFold, Boltz-2 and ESM for antibody modeling, co-folding, binder prediction and developability
• Turn research code and prototypes into reliable components that run in federated training and evaluation pipelines
• Design evaluations and benchmarks, and deliver results packages for consortium partners
• Own workstreams through to release against agreed milestones, raising risks and trade-offs early
• Work with product, engineering, research and consortium members to ensure model work meets real application needs
• Build, train and evaluate ML systems for antibody modeling, co-folding, developability prediction and biologics discovery
• Work with proprietary pharmaceutical data across federated networks
• Convert research-led or open-source prototypes into models that can be evaluated, released and used in real drug discovery workflows

• An MSc, PhD or equivalent experience in machine learning, computational biology, bioinformatics, physics or a related field
• Strong Python and PyTorch
• Hands-on experience training or fine-tuning deep learning models on biomolecular data
• Hands-on experience with co-folding models or protein language models such as OpenFold, AlphaFold, Boltz, ESM or similar, beyond just running inference
• Good evaluation habits and solid engineering practice, including fair benchmarks, reproducible experiments, and maintainable code
• Experience with Kubernetes-based training, evaluation or deployment, or other MLOps and ML infrastructure tooling is nice to have
• Experience with federated learning, privacy-preserving ML, or distributed and multi-GPU training is nice to have
• Experience in pharma, biotech or other regulated or high-trust environments is nice to have
• Publications in ML, computational biology or structural biology venues such as NeurIPS, ICML, ICLR or similar are nice to have

• Remote work (UTC +/- 2 hrs)
• Full-time permanent employment