Advisor - Applied Deep Learning Architect

Eli Lilly UK

San Diego, California, US
Base: $151,500 - $222,200; bonus/equity: compyny b...
On-site
Deep learning architectures
Transformers, diffusion models, flow-matching models, graph neural networks
Multi-modal embeddings
Design, implement, and evaluate generative and predictive deep learning architectures—transformers, diffusion models, flow-matching models, and graph neural networks

Job Summary

  • Design, implement, and evaluate generative and predictive deep learning architectures—transformers, diffusion models, flow-matching models, and graph neural networks.
  • Develop multi-modal embeddings that unify protein sequence, structure, and molecular fingerprints, researching novel tokenization schemes and fusion mechanisms.
  • Partner with internal MD scientists to integrate physics-based priors, molecular dynamics, and energy-aware learning objectives into model training.

Matching Summary

Design, implement, and evaluate generative and predictive deep learning architectures—transformers, diffusion models, flow-matching models, and graph neural networks.

Salary

Base: $151,500 - $222,200; Bonus/Equity: company bonus; Benefits: comprehensive benefit program

Skills & Requirements

Must-have

  • deep learning architectures
  • transformers, diffusion models, flow-matching models, graph neural networks
  • multi-modal embeddings
  • protein sequence, structure, and molecular fingerprints
  • Python and modern AI/ML frameworks (PyTorch or TensorFlow)

Nice-to-have

  • protein engineering
  • protein language models
  • generative protein models
  • multi-modal architectures
  • molecular dynamics simulations
  • high-impact publications
  • cross-functional collaboration

Key Requirements

  • Ph.D. in Computer Science, Artificial Intelligence, Theoretical Computer Science, Applied Mathematics, Computational Biology, Physics, or a related field
  • 1-3 years of industry experience in development and deployment of Novel Deep Learning Architecture
  • Familiarity with protein engineering, protein sequence and structure representation
  • Experience applying ML to antibody, nanobody, or peptide design
  • Experience with multi-modal architectures
  • Experience integrating molecular dynamics simulations
  • Experience with distributed training, GPU-accelerated workflows
  • Good software engineering practices including Git version control, code review, testing, and documentation

Work Rights

Not specified

Tailored Resume

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