Lead Scientist - Large Molecules

Role overview A federated computing and AI platform for pharmaceutical research is hiring a large-molecule specialist to lead scientific direction across antibody-related programs. The role sits at the intersection of antibody engineering, structural biology, and applied machine learning, owning what is scientifically relevant while partnering with internal ML and engineering teams who build the actual models. The position is well suited to a domain expert who wants to shape modeling strategy and partner credibility without writing training code themselves.

Responsibilities - Define scientific workflows, evaluation strategies, and benchmarking approaches for large-molecule programs including antibody-antigen co-folding, binder prediction, and antibody developability - Act as a hands-on user of internal tooling and translate real laboratory needs into concrete product requirements for large-molecule workflows - Drive adoption with pharma partners by identifying relevant use cases and supporting them in extracting value from deployed models - Convert scientific and biological questions from partners into clearly scoped inputs that ML and engineering teams can build against - Review model outputs and benchmark results against antibody engineering and structural biology knowledge, flagging where biology does not hold up - Represent the organization's scientific perspective in partner conversations, aligning on objectives, evaluation criteria, and data requirements - Stay current on the large-molecule AI/ML landscape and bring informed opinions to modeling decisions - Collaborate with product, ML, and engineering to ensure scientific requirements genuinely shape the roadmap

Requirements - PhD or equivalent experience in structural biology, protein engineering, or a closely related biologics discipline - Hands-on experience in antibody design, developability, or binder discovery - Sufficient AI/ML fluency to engage with modeling workflows, judge whether outputs make sense, and translate biological questions into actionable plans - Comfort working closely with ML and engineering teams and bridging biological reasoning with technical implementation - Clear communication across scientific, technical, and pharma stakeholder audiences

Nice to have - Familiarity with structure prediction tools such as OpenFold, AlphaFold, or Boltz - Experience with antibody developability assays, immunogenicity, or biologics manufacturability - Prior work with pharma partners or in a consortium or collaborative research setting - Publication record in structural biology, immunology, or antibody engineering venues

Benefits and work setup - Competitive compensation including early-stage virtual share options - Remote-first working with flexibility on location - Wellbeing support covering mental health resources, a home-office budget, co-working stipend, and learning budget - Generous holiday allowance - Optional in-person office days roughly three times per year - Execution-focused team environment