At Apheris, we are building the future of how AI is applied in pharmaceutical R&D.
We enable leading pharmaceutical teams to discover and develop drugs faster. We host the industry’s largest federated data networks for drug discovery AI, spanning co-folding, ADMET, and antibody developability.
Across these networks, models are trained on proprietary industry datasets to achieve higher performance and broader applicability while keeping data control and IP protected. We deliver these superior models through drug discovery applications that enable teams to run them at scale, further customize them, and integrate them into existing R&D workflows.
- AI Structural Biology (AISB) Network: Pharmaceutical companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.
- ADMET Network: Pharmaceutical and biotech companies collaborate to improve small-molecule property prediction and expand to further drug modalities.
- Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.
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.
You're an ML engineer who works close to the science. You've trained and evaluated models on biological data, and you can take a paper or an open-source model and make it work on a new problem.
You care about whether a model actually holds up, not just whether the numbers look good, and you want your work to end up in real use by pharma R&D teams.
- 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.
- 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.
- 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.