Hybrid AI Engineer for Drug-Discovery Platforms

Eli Lilly; San Francisco (CA); Hybrid; USD 141,000 - 253,000; Full time

Benefits offered by this job

Company bonus

401(k)

Pension

Vacation benefits

Medical, dental, vision

Flexible benefits

Life insurance

Leave benefits

Well-being programs

Job summary

Eli Lilly, in partnership with NVIDIA, is seeking an AI Engineer to turn advanced AI modeling into dependable drug-discovery tools within a Lilly–NVIDIA AI co-innovation lab. Based in Silicon Valley, you will collaborate with AI scientists and engineering teams to deploy end-to-end AI systems for research workflows.

You will lead distributed training, model evaluation, and productization, while upholding strict data privacy, ethics, and security standards in a hybrid onsite/remote environment.

Qualifications

• Advanced Python and production ML experience.
• Experience moving research to production systems.
• Distributed training on multi-GPU/multi-node infrastructure.
• Experience serving models for inference with containerization.

Responsibilities

• Develop and apply innovative AI techniques for clinical research processes.
• Build and deploy AI-driven models and applications across the full stack.
• Design resilient architectures for performance, scalability, and security.
• Support large-scale model development and benchmarking.
• Collaborate with researchers to drive AI innovation.
• Ensure compliant AI data handling and privacy.

Skills

Advanced Python

PyTorch/JAX

Multi-GPU training

Model evaluation/benchmarking

Full stack capability

JavaScript/React

NumPy/SciPy/Pandas

C++/CUDA (a plus)

Cloud platforms (AWS/Azure/GCP)

Education

Master’s degree in Computer Science/ML/AI/Data Science/Engineering

Tools

Docker/Kubernetes

DDP/FSDP/DeepSpeed

Megatron/Triton/vLLM

Job description

Eli Lilly, in partnership with NVIDIA, is seeking an AI Engineer to turn advanced AI modeling into dependable drug-discovery tools within a Lilly–NVIDIA AI co-innovation lab. Based in Silicon Valley, you will collaborate with AI scientists and engineering teams to deploy end-to-end AI systems for research workflows.

You will lead distributed training, model evaluation, and productization, while upholding strict data privacy, ethics, and security standards in a hybrid onsite/remote environment.