We are seeking a scientifically grounded expert in structure-based drug discovery with a strong user-first, proactive mindset.
This role is central to driving adoption and boosting usage of the Co-Folding Application within leading pharma R&D organizations. You will act as a power user of Apheris products yourself, and will guide computational and medicinal chemists in benchmarking, testing, and applying our models to real programs. Your focus will be on anticipating user needs, engaging the right stakeholders, and making sure pharma scientists and users not only try the application but rely on it in their day-to-day work.
You will collaborate closely with pharma users, internal ML scientists, the AI Applications Team, and the Product team to embed the Co-Folding Application into existing R&D environments. If you want to accelerate the use of AI in drug discovery — and you thrive on working directly with users to ensure adoption translates into measurable value — this role is for you.

• Industry-competitive compensation, including early-stage virtual share options
• Remote-first working – work where you work best, whether from home or a co-working space near you
• Great suite of benefits, including a wellbeing budget, mental health benefits, a work-from-home budget, a co-working stipend and a learning and development budget
• Regular team lunches and social events
• Generous holiday allowance
• Office Days at our Berlin HQ or a different European location (3x a year)
• A fun, diverse team of mission-driven individuals with a drive to see AI and ML used for good
• Plenty of room to grow personally and professionally and shape your own role

Interested?
Please reach out to José-Tomás (JT) Prieto (j.prieto@apheris.com), Director of AI Programs, directly, including a current version of your CV and a short description why you would be a great fit for this role.

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.