PhD ML Engineering Intern — AI Systems & Compute

100 Eli Lilly and Company; Indianapolis (IN); On-site; USD 120,000 - 136,000; Full time

Benefits offered by this job

Free parking

Lilly LIFE fitness center

Bike garage

Subsidized housing

Job summary

Lilly is seeking a PhD candidate to join the ML Engineering team as an intern, supporting Lilly’s Research AIOS. You will work on sizing parallel replicas, data partitioning, and resumable execution tooling for a heterogeneous GPU/CPU compute fleet, under mentorship.

This 12-week, full-time internship emphasizes hands-on development in AI/ML systems engineering and parallel computing. You’ll gain experience with GPU computing, distributed scheduling, and production-grade code delivery while

Qualifications

• PhD in Computer Science, Applied Mathematics, Physics, Engineering, or related field with at least 2 years of graduate research completed.
• Coursework or research in HPC, distributed systems, or ML systems engineering.
• Experience with containerization (Docker).
• Exposure to batch/distributed job execution in HPC schedulers or Kubernetes.

Responsibilities

• Contribute to the design, implementation, and testing of platform capabilities within the Compute domain.
• Build and ship features for a compute platform’s job-sizing, data-partitioning, and resumable-execution tooling under guidance from a mentor.
• Write well-tested, production-quality code following team standards and participate in code reviews and design discussions.

Skills

Python

Parallel computing

Distributed computing

Git/version control

Education

PhD in Computer Science or related field

Tools

Docker

Job description

Lilly is seeking a PhD candidate to join the ML Engineering team as an intern, supporting Lilly’s Research AIOS. You will work on sizing parallel replicas, data partitioning, and resumable execution tooling for a heterogeneous GPU/CPU compute fleet, under mentorship.

This 12-week, full-time internship emphasizes hands-on development in AI/ML systems engineering and parallel computing. You’ll gain experience with GPU computing, distributed scheduling, and production-grade code delivery while