Functional OverviewTech[redacted] builds and maintains capabilities using cutting edge technologies like most prominent tech companies. What differentiates Tech[redacted] is that we redefine what's possible through tech to advance our purpose - creating medicines that make life better for people around the world, like data driven drug discovery and connected clinical trials. We hire the best technology professionals from a variety of backgrounds, so they can bring an assortment of knowledge, skills, and diverse thinking to deliver innovative solutions in every area of our business.
The Advanced Intelligence organization sits at the forefront of Lilly's AI transformation, driving artificial intelligence and machine learning to revolutionize how we discover, develop, and deliver medicines to patients. Leading initiatives from AI-enabled laboratories to Kernel Lilly-the AI-enabled research infrastructure stack serving as the operating system for pharmaceutical research-to strategic partnerships with world-class technology companies, to proprietary foundation models trained on Lilly's research data, this team is building the technical foundation for AI-powered R&D. With deep expertise in cutting-edge AI technologies and pharmaceutical science, Advanced Intelligence is transforming Lilly into an AI-first drug discovery organization, accelerating breakthrough treatments to patients.
You will work as a member of the ML Engineering team supporting Lilly's Research AIOS, reporting to an Engineering Director. You'll contribute to the Compute domain of the AI Operating System - specifically to the tooling that decides, at job-submission time, how a model gets scaled out and run reliably across a large, heterogeneous, non-autoscaling GPU/CPU fleet. You'll build automation that improves the efficiency and resiliency of prototyped models - graph-based, diffusion, flow-matching, and language models among them - as they move toward broader production use. The core of your work will focus on one connected problem with three parts: automatically sizing how many parallel replicas and which hardware to use, partitioning a dataset across those replicas without manual sharding, and making batch jobs resumable so an interruption only re-does the remaining work instead of starting over.
Basic Qualifications - Currently pursuing a PhD in Computer Science, Applied Mathematics, Physics, Engineering, or a related field, with at least 2 years of graduate research completed
- Coursework or research experience in at least one of: high-performance/parallel computing, distributed systems, or ML systems engineering
- Experience with containerization (Docker)
- Exposure to batch/distributed job execution in at least one of: traditional HPC job schedulers (e.g., Slurm, Grid Engine) or running batch workloads in a Kubernetes environment
- Available for 12 weeks full-time engagement between May-September 2027
Additional Preferences- Prior experience using AI tools (e.g., generative AI platforms, automation tools, or AI-assisted research/analytics tools) in an academic, project, or work setting
How You'll SucceedContribute to the design, implementation, and testing of platform capabilities within the Compute domain.
- You will build and ship features for a compute platform's job-sizing, data-partitioning, and resumable-execution tooling under guidance from your mentor
- You will write well-tested, production-quality code following team architectural standards and design patterns
- You will participate in code reviews, design discussions, and sprint ceremonies
Learning & GrowthDevelop technical depth in ML systems engineering while gaining pharma R&D context.
- You will gain hands-on experience with GPU computing, heterogeneous-cluster scheduling, and distributed/parallel execution of batch workloads
- You will learn how resource-sizing, data-partitioning, and fault-tolerance decisions affect platform performance, cost, and reliability across diverse model architectures
- You will document your work and share learnings with the broader team
CollaborationWork effectively within a cross-functional engineering team.
- You will pair with senior engineers on complex problems, asking questions and contributing ideas
- You will participate in stand-ups, retrospectives, and planning sessions
- You will present your intern project outcomes to the team and leadership at the end of the rotation
What You Should Bring- Curiosity about AI/ML systems and how they're built at scale
- Foundational programming skills in Python, and comfort with parallel or distributed computing concepts (e.g., MPI, multiprocessing, or distributed data/task frameworks)
- Exposure to machine learning concepts and at least one class of generative or predictive model architecture (coursework, personal projects, or research)
- Familiarity with version control (Git) and collaborative development workflows
- Strong communication skills - ability to ask good questions and document what you learn
- Self-direction balanced with knowing when to ask for help
- Interest in pharmaceutical research or life sciences applications of AI (preferred, not required)
- As part of Lilly's commitment to innovation, interns will have the opportunity to build fluency with AI tools used across the business. We expect interns to approach these tools with curiosity, apply critical thinking to AI-assisted work, and always prioritize accuracy, confidentiality, and ethical standards in how they use them.
- Lilly arranges various intern activities including sporting events, dinners, lunch and learns, volunteer activities etc. to provide opportunities for socializing, professional development, and learning more about Lilly.
- Interns will receive 1 week of paid time off during the Lilly summer shut-down (July 5th - July 9th)
- 1:1 mentoring from an experienced professional in the function
- Interns will receive a competitive salary and free parking at their work site, as well as access to Lilly's LIFE fitness center, bike garage, and many other discounts
- If the intern's job position requires a move from another location, Lilly will provide subsidized housing
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$119,850 - $136,000
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including 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; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
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