This is a fully remote position, open to applicants in Germany.

📋 Description

• Develop, refine, and expand large biomolecular models such as OpenFold, Boltz-2, and ESM for antibody modeling, co-folding, binder prediction, and assess their developability.

• Transform research code and prototypes into dependable components that function within federated training and evaluation pipelines.

• Create evaluations and benchmarks, delivering results packages to consortium partners.

• Manage workstreams through to release, adhering to agreed milestones while proactively identifying risks and trade-offs.

• Collaborate with product, engineering, research, and consortium members to ensure that model development aligns with practical application needs.

• Construct, train, and assess ML systems for antibody modeling, co-folding, developability prediction, and biologics discovery.

• Utilize proprietary pharmaceutical data across federated networks.

• Transform research-driven or open-source prototypes into models that can be assessed, released, and integrated into actual drug discovery workflows.

⛳️ Requirements

• An MSc, PhD, or equivalent experience in machine learning, computational biology, bioinformatics, physics, or a related discipline.

• Proficiency in Python and PyTorch.

• Practical experience in 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 merely executing inference.

• Strong evaluation practices and solid engineering skills, including fair benchmarking, reproducible experiments, and maintainable code.

• Familiarity with Kubernetes-based training, evaluation, or deployment, or other MLOps and ML infrastructure tools is advantageous.

• Experience with federated learning, privacy-preserving ML, or distributed and multi-GPU training is a plus.

• Background in pharma, biotech, or other regulated or high-trust environments is beneficial.

• Publications in ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, or similar are advantageous.