[Hiring] ML Engineer @Apheris

Role Description

We are looking for an ML Engineer to join our large molecule ML team and build the models behind our antibody, co-folding and developability programs. This is a hands-on role at the intersection of foundation models, structural biology, protein engineering and federated learning. You will build, train and evaluate ML systems for antibody modeling, co-folding, developability prediction and biologics discovery, working on proprietary pharma data across our federated networks.

You will own substantial parts of our model programs end to end. That means taking research-led or open-source prototypes and turning them into models that can be evaluated, released and used in real drug discovery workflows.

Qualifications

• An MSc, PhD or equivalent experience in machine learning, computational biology, bioinformatics, physics or a related field
• Strong Python and PyTorch, and 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: fair benchmarks, reproducible experiments, and code other people can build on

Requirements

• 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 our 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 make sure the model work meets real application needs

Nice to have

• Experience with Kubernetes-based training, evaluation or deployment, or other MLOps and ML infrastructure tooling
• Experience with federated learning, privacy-preserving ML, or distributed and multi-GPU training
• Experience in pharma, biotech or other regulated or high-trust environments
• Publications in ML, computational biology or structural biology venues such as NeurIPS, ICML, ICLR or similar

Benefits

• Logistics
• Our mission statement