Advisor – Applied Deep Learning Architect

Role Overview

• 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
• Develop multi-modal embeddings that unify protein sequence, structure, and molecular fingerprints
• Research novel tokenization schemes and fusion mechanisms that improve both generation quality and property prediction
• Research approaches for jointly modeling proteins and small molecules within the foundational architecture
• Enable applications to ADCs, antibody–peptide conjugates, T-cell engagers, and other multi-component biotherapeutic formats
• Partner with internal MD scientists to integrate physics-based priors, molecular dynamics, and energy-aware learning objectives into model training
• Stay at the frontier of AI/ML and computational biology research; identify high-impact directions that advance the foundational model platform
• Educate and transfer knowledge to other domain experts effectively to drive cross-functional collaboration on cutting-edge science.

Requirements

• Ph.D. in Computer Science, Artificial Intelligence, Theoretical Computer Science, Applied Mathematics, Computational Biology, Physics, or a related field
• Strong expertise in modern deep learning architectures, including transformers, diffusion models, flow-matching networks, variational autoencoders, and graph neural networks
• 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
• 1-3 years of industry experience in development and deployment of Novel Deep Learning Architecture
• 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
• 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)
• Protein structure understanding; experience
• Experience integrating molecular dynamics simulations, force-field representations, or physics-based priors into machine learning models for molecular design or optimization
• Experience with distributed training, GPU-accelerated workflows, and writing performant code for large-scale model training and inference
• Prior exposure to experimental biologics workflows (phage display, yeast display, directed evolution) that informs practical design constraints is a plus
• Demonstrated history of high-impact publications in top-tier machine learning, AI, or computational biology venues
• Strong oral and written communication skills, with the ability to effectively communicate technically challenging concepts and ideas with team members across expert disciplines.

Tech Stack

• Chai
• Python
• PyTorch
• Tensorflow

Benefits

• 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
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)