Advisor - Applied Deep Learning Architect

Responsibilities Deep Learning Architecture Analysis & Direction: Design, implement, and evaluate generative and predictive deep learning architectures—transformers, diffusion models, flow-matching models, and graph neural networks. Share actionable strategies and drive architectural decisions to improve the performance of foundational models for biologics drug discovery. Multi-Modal Representation Learning: Develop multi-modal embeddings that unify protein sequence, structure, and molecular fingerprints, researching novel tokenization schemes and fusion mechanisms that improve both generation quality and property prediction. Cross-Modality Molecular Modeling: Research approaches for jointly modeling proteins and small molecules within the foundational architecture, enabling applications to ADCs, antibody–peptide conjugates, T‑cell engagers, and other multi‑component biotherapeutic formats. Physics-Informed Training Objectives: Partner with internal MD scientists to integrate physics‑based priors, molecular dynamics, and energy‑aware learning objectives into model training to ground generative outputs in physical reality and improve the developability of designed molecules across complex modalities. Scientific Scouting & Knowledge Transfer: Stay at the frontier of AI/ML and computational biology research; identify high‑impact directions that advance the foundational model platform and translate them into actionable strategies for the team. Educate and transfer knowledge to other domain experts effectively to drive cross‑functional collaboration on cutting‑edge science. Basic Qualifications Education: Ph.D. in Computer Science, Artificial Intelligence, Theoretical Computer Science, Applied Mathematics, Computational Biology, Physics, or a related field. Deep Learning Expertise: Strong expertise in modern deep learning architectures, including transformers, diffusion models, flow‑matching networks, variational autoencoders, and graph neural networks. Technical Skills: Proficiency in Python and modern AI/ML frameworks (PyTorch or TensorFlow). Familiarity with good software engineering practices including Git version control, code review, testing, and documentation. Preferred Qualifications Experience: 1–3 years of industry experience in development and deployment of Novel Deep Learning Architecture. Biologics or Protein Science: Familiarity with protein engineering, protein sequence and structure representation, protein language models (e.g., ESM, AbLang), generative protein models (RFDiffusion, Boltz, Chai, etc.) or related biomolecular ML. Experience applying ML to antibody, nanobody, or peptide design is strongly preferred. Multi‑Modal Modeling: Experience with multi‑modal architectures that jointly model sequence, structure, and functional annotations, and that fuse molecular representations across different molecule modalities (e.g., protein‑peptide, protein‑ligand, protein‑small molecule). Molecular Dynamics & Physics‑Informed ML: Experience integrating molecular dynamics simulations, force‑field representations, or physics‑based priors into machine learning models for molecular design or optimization. Distributed & High‑Performance Computing: Experience with distributed training, GPU‑accelerated workflows, and writing performant code for large‑scale model training and inference. Wet‑Lab Awareness: Prior exposure to experimental biologics workflows (phage display, yeast display, directed evolution) that informs practical design constraints is a plus. Publication Track Record and Communication Skills: Demonstrated history of high‑impact publications in top‑tier machine learning, AI, or computational biology venues. Communication Skills: Strong oral and written communication skills, with the ability to effectively communicate technically challenging concepts and ideas with team members across expert disciplines. Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status. Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is $151,500 - $222,200. Full‑time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company‑sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well‑being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities). Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees. #J-18808-Ljbffr Eli Lilly