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. 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.
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. 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.
- 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
- Feed insights from the field back to Product and the AI Applications Team to shape platform improvements
- Run in-person and remote workshops to train users and share best practices with research teams
- Develop case studies that demonstrate impact and can be replicated across projects
- Contribute scientific content such as example datasets, usage guidelines, and evaluation protocols
- PhD in computational chemistry, medicinal chemistry, or a related field
- Hands-on experience as a power user in structure-based workflows - co-folding, docking, protein–ligand modeling in real drug discovery programs
- Familiarity with molecular visualization and cheminformatics tools (e.g., PyMOL, RDKit)
- Credibility with both computational and medicinal chemists, and ability to bridge what each group needs
- Basic scripting skills to run and explain notebooks and analyses
- Comfortable working without full specifications - takes ownership, makes progress under ambiguity
- Communicates clearly across technical and non-technical audiences
- Experience in a customer-facing role (e.g., CRO, application scientist)
- Familiarity with OpenFold, Boltz, or other structure/affinity prediction tools
- Experience working within or alongside enterprise IT/data environments typical of large pharma
- Experience working on time-sensitive or high-stakes projects
- 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
PhD in computational/medicinal chemistry, expertise in structure-based workflows (co-folding, docking, protein–ligand modeling), scripting and notebooks, customer-facing experience, strong communication and ownership under ambiguity.
Key Responsibilities
- driving adoption
- building relationships
- running workshops
Skills & Tools
PyMOL, RDKit, OpenFold3, Boltz-2, OpenFold, Boltz
Job Details
- Category: Research and Development (R&D)
- Seniority: Senior Level
- Commitment: Full Time
- Workplace: Remote — Berlin or Europe
- Languages: English
Benefits
- Generous paid time off
About Apheris
A company providing federated data networks and drug discovery applications to enable collaborative, privacy-preserving machine learning for life sciences. — Industry: Research and Development