Public summary
A growing pharmaceutical AI company is seeking a Lead Scientist specializing in large molecules to contribute domain expertise in antibody engineering, structural biology, and biologics. The role involves defining scientific workflows and modeling strategies for antibody-related projects, collaborating closely with machine learning and engineering teams, and interfacing with pharmaceutical partners to ensure scientific credibility and practical application of AI models in drug discovery. The position offers competitive compensation with share options, remote-first work options, and various wellbeing and learning benefits.
Location and work setup
- Location
- Berlin
- Remote status
- Remote
- German requirement signal
- No German Required Detected
- Detected job language
- English
Responsibilities
Define scientific workflows, evaluation strategies, and benchmarking for large molecule projects such as antibody-antigen co-folding and developability prediction. Act as the hands-on user to ensure development aligns with scientific needs. Drive adoption of large molecule models with pharma partners by identifying relevant programs and supporting value realization. Translate scientific and biological requirements into concrete inputs for the ML and engineering teams. Review model outputs for biological validity and represent the scientific perspective in partner discussions. Stay informed on advances in large molecule AI/ML techniques and collaborate with product and engineering teams to incorporate scientific priorities into roadmaps.
Qualifications
PhD, MSc, or equivalent with 5+ years of experience in structural biology, antibody engineering, immunology, protein engineering or related biologics fields. Hands-on expertise in antibody design, developability, or binder discovery. Familiarity with AI/ML applications in biology sufficient to contribute meaningfully to modeling workflows without needing to build models. Ability to communicate effectively across scientific, technical, and partner audiences. Preferred experience includes knowledge of structural prediction tools (e.g., OpenFold, AlphaFold), experience with antibody developability assays, and collaborative research or pharma partnership experience.