About Apheris
Apheris builds AI for pharmaceutical R&D, helping pharmaceutical teams discover and develop drugs faster. It hosts federated data networks for drug discovery AI and provides models through applications that support large-scale use, customization, and integration into existing R&D workflows.
Responsibilities
•Build, fine-tune, and extend large biomolecular models for antibody modeling, co-folding, binder prediction, and developability
•Turn research code and prototypes into reliable components for federated training and evaluation pipelines
•Design evaluations and benchmarks, and deliver results packages for consortium partners
•Own workstreams through 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
Requirements
•MSc, PhD, or equivalent experience in machine learning, computational biology, bioinformatics, physics, or a related field
•Strong Python and PyTorch skills
•Hands-on experience training or fine-tuning deep learning models on biomolecular data
•Hands-on experience with co-folding models or protein language models beyond running inference
•Good evaluation practices, including fair benchmarks and reproducible experiments
•Solid engineering practices and the ability to write code others can build on
Nice to have
•Kubernetes-based training, evaluation, or deployment
•MLOps or ML infrastructure tooling
•Federated learning
•Privacy-preserving ML
•Distributed or 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, or ICLR
Conditions
No conditions specified in the posting.
