AI PhD ML Engineering Intern

Eli Lilly and Company

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Indianapolis, IN
Salary
$119,850–$136,000 / yr
Employment
Full-time
Posted
34 days ago
Freshness
Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $194k
This role $128k
$105k most similar roles pay here $256k

This role pays less than 81% of similar roles. Most pay $146,500–$241,750 — the shaded band above. At the midpoint, this role pays about $128k versus about $194k for comparable roles.

Based on 240 similar postings.

Employer

About Eli Lilly and Company

Eli Lilly and Company is a global pharmaceutical company that discovers, develops, and markets medicines in areas such as diabetes, oncology, neuroscience, and immunology, known for products like insulin and Mounjaro. Industry: Pharmaceuticals

Eli Lilly and Company currently has 4 open roles on FindRole.

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At a glance

TL;DR · AI PhD ML Engineering Intern

JOB TITLE: AI PhD ML Engineering Intern The AI PhD ML Engineering Intern joins the ML Engineering team to support the Research AIOS, a core infrastructure stack for pharmaceutical research. This role focuses on the Compute domain, where the intern will build automation to improve the efficiency and resiliency of prototyped models, including graph-based, diffusion, flow-matching, and language models. Key responsibilities include developing tooling for automatic job-sizing across heterogeneous GPU/CPU fleets, implementing automated data-partitioning without manual sharding, and creating resumable-execution features for batch jobs. The candidate will utilize Python, Git, Docker, and concepts from high-performance computing, distributed systems, and ML systems engineering. This work addresses the technical challenge of scaling and reliably running complex models within a large-scale, non-autoscaling hardware environment to accelerate drug discovery.

What you'll do

  • Design, implement, and test platform capabilities within the Compute domain of the AI Operating System.
  • Build and ship features for job-sizing, data-partitioning, and resumable-execution tooling.
  • Write well-tested, production-quality code following established architectural standards and design patterns.
  • Automate the scaling and reliable execution of models across heterogeneous GPU/CPU fleets.
  • Partition datasets across replicas automatically without requiring manual sharding.
  • Develop features to make batch jobs resumable to prevent redundant work during interruptions.
  • Participate in code reviews, design discussions, and sprint ceremonies.
  • Document technical work and present project outcomes to the team and leadership.

What we're looking for

  • Currently pursuing a PhD in Computer Science, Applied Mathematics, Physics, Engineering, or a related field.
  • Completed at least 2 years of graduate research.
  • Coursework or research experience in high-performance/parallel computing, distributed systems, or ML systems engineering.
  • Experience with containerization using Docker.
  • Exposure to batch/distributed job execution in traditional HPC job schedulers or Kubernetes environments.
  • Foundational programming skills in Python and comfort with parallel or distributed computing concepts.
  • Exposure to machine learning concepts and at least one class of generative or predictive model architecture.
  • Familiarity with Git version control and collaborative development workflows.
  • Prior experience using AI tools in an academic, project, or work setting (preferred).
  • Interest in pharmaceutical research or life sciences applications of AI (preferred).

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