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Apheris is building AI-driven drug discovery platforms that enable pharmaceutical teams to discover and develop drugs faster. The company hosts federated data networks for drug discovery AI, including the AI Structural Biology (AISB) Network, ADMET Network, and Antibody Developability Network, where models are trained on proprietary industry datasets while keeping data control and IP protected.
You will serve as the large molecule specialist, bringing deep domain expertise in antibody engineering, structural biology, and biologics to define scientific workflows and modeling strategy for antibody-antigen co-folding, binder prediction, and developability prediction. Working closely with the ML and engineering teams (who own model-building), you will give large molecules a dedicated point of ownership at Apheris and serve as the trusted scientific voice with pharma partners.
Key responsibilities include:
- Define the scientific workflow, evaluation strategy, and benchmarking approach for large molecule programs (antibody-antigen co-folding, binder prediction, antibody developability)
- Use the product as a hands-on user and define user requirements for large molecule workflows
- Drive adoption of large molecule models with pharma partners, helping them identify relevant use cases and supporting value realization
- Translate scientific and biological requirements from partners into concrete inputs for the ML/engineering team
- Review model outputs and evaluation results against structural biology and antibody engineering knowledge
- Represent Apheris's scientific perspective in partner conversations across large molecule networks
- Stay current on the large molecule AI/ML landscape (OpenFold, AlphaFold, Boltz, ESM, antibody design/developability literature)
- Partner with product, ML, and engineering to ensure scientific requirements shape the roadmap
You do not need to write training code or build models yourself, but you must have enough AI/ML fluency to sanity-check results, catch where modeling approaches don't reflect biological reality, and help translate scientific questions into workable plans.
REQUIREMENTS:
- PhD, MSc, or equivalent experience plus 5+ years in structural biology, antibody engineering, immunology, protein engineering, or related biologics discipline
- Real hands-on experience in antibody design, developability, or binder discovery (domain-knowledge-first role)
- Exposure to applying AI/ML to biological problems; understand models well enough to contribute to modeling workflows and judge whether outputs make sense
- Comfortable partnering closely with ML/engineering teams and translating between biological reasoning and technical implementation
- Clear communication across scientific and technical audiences, and with pharma partner stakeholders
NICE-TO-HAVE:
- Familiarity with OpenFold, AlphaFold, Boltz, or similar structure prediction tools
- Experience with antibody developability assays, immunogenicity, or biologics manufacturability
- Direct work with pharma partners or in consortium/collaborative research settings
- Publication record in structural biology, immunology, or antibody engineering
