About Apheris

At Apheris, we power federated data networks in life sciences to address the data bottleneck in training highly performant ML models. Publicly available, molecular datasets are insufficient to train high-quality ML models that meet industry requirements. We address this by hosting networks where pharma organizations collaboratively train higher quality models on their combined data.

The Apheris product is a set of drug discovery applications enriched with the proprietary data of network participants. Our federated computing infrastructure with built-in governance and privacy controls ensures that the data IP and ownership always stay with the data custodians.

We currently host two flagship networks: the AISB Network, focused on protein co-folding and binding affinity prediction, and the ADMET Network, focused on small-molecule property prediction. In addition, we have just launched our Co-Folding Application, which enables pharma teams to deploy models like OpenFold3 and Boltz-2 directly in their own environments - with more capabilities to follow.

About the role

We are looking for a structure-based drug discovery expert to drive adoption of the Co-Folding Application inside leading pharma R&D teams. You will be a power user of Apheris products yourself, and you will work directly with computational and medicinal chemists, helping them test, benchmark, and apply our models to real programs, ensuring the product is used effectively and productively as part of their day-to-day work.

You will work closely with pharma users, internal ML scientists, the AI Applications Team, and Product to get the Co-Folding Application embedded into existing R&D workflows. If you want to see AI used in drug discovery, not just deployed, this role is for you.

What you will do

Act as a forward-deployed scientist, focused on driving adoption and real, measurable impact
Work hands-on with pharma users of the Co-Folding Application, guiding benchmarking, testing, and application to real discovery programs
Build relationships with the relevant stakeholders across pharma R&D, from computational and medicinal chemists to program leaders