Job description

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley! Making medicine that’s never been made means doing what’s never been done. If you’re an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation; we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us! About the Lilly and NVIDIA Partnership Lilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery’s toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe. What You’ll Be Doing As an ML Ops Engineer, you build and operate the platforms that run the end-to-end machine learning lifecycle. You enable reliable model deployment, operation, monitoring, retraining, and reproducibility at scale. You optimize infrastructure and GPU resources to support research and discovery workloads. You will work closely with engineering and scientific teams to deliver production-ready AI capabilities. How You’ll Succeed Lead the operational lifecycle of ML models, including deployment, monitoring, and ongoing reliability. Operate and optimize large-scale inference platforms that support scientific discovery and AI workloads. Ensure models can be deployed, scaled, monitored, and maintained in production environments. Test, refine, and improve model accuracy. Work with data scientists, business analysts and partners to integrate ML models into broader strategies. Automate the platform with infrastructure-as-code and CI/CD, and document it well enough that someone else can operate it. What You Should Bring Strong Python skills and experience working with machine learning frameworks such as PyTorch, JAX, or TensorFlow. Experience deploying, operating, and scaling production machine learning platforms, including model serving, monitoring, and large-scale inference workloads. Experience with MLOps platforms and tools like MLflow, Weights & Biases, KServe, or similar technologies. Proficiency with containerization, orchestration, and distributed compute environments (Docker, Kubernetes, Slurm, Ray). Experience operating large-scale AI platforms that deploy, host, and optimize machine learning models for production use, using technologies such as Triton, vLLM, or TensorRT-LLM. Experience with infrastructure automation and CI/CD practices using tools such as Terraform, Ansible, GitHub Actions, or related. Experience supporting cloud platforms (AWS, Azure, or GCP) and on-premises GPU infrastructure. Knowledge of observability and operational monitoring, including metrics, logging, tracing, and performance tuning. Ability to identify and address system, infrastructure, and model performance issues through automation and continuous improvement. Ability to collaborate effectively with research scientists, AI engineers, and infrastructure teams in a fast-paced environment. Your Basic Qualifications Bachelor’s in Computer Science, Engineering, Statistics, Mathematics, or a related technical field 4 years of experience in machine learning engineering, ML Ops, or platform engineering. Location & Work Flexibility This role is based at our Silicon Valley Hub. We offer a flexible hybrid work model, with three days onsite and two days working remotely each week , supporting both collaboration and work‑life balance. Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( careers.lilly.com ) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response. Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status. Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia (AMECA), Black Employees at Lilly (BE Lilly), Chinese Culture Network (CCN), EnAble, Evolve, Lilly Indian Network (LIN), Organization of Latinx at Lilly (OLA), Pride (LGBTQ Allies), Veterans Leadership Network (VLN) and Women’s Initiative for Leading at Lilly (WILL). Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is $147,000 - $268,400 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. #WeAreLilly

Requirements

Must-haves

Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley!
About the Lilly and NVIDIA Partnership
What You’ll Be Doing
How You’ll Succeed
Lead the operational lifecycle of ML models, including deployment, monitoring, and ongoing reliability.
Operate and optimize large-scale inference platforms that support scientific discovery and AI workloads.
Ensure models can be deployed, scaled, monitored, and maintained in production environments.
Test, refine, and improve model accuracy.
Work with data scientists, business analysts and partners to integrate ML models into broader strategies.
Automate the platform with infrastructure-as-code and CI/CD, and document it well enough that someone else can operate it.
What You Should Bring
Strong Python skills and experience working with machine learning frameworks such as PyTorch, JAX, or TensorFlow.
Experience deploying, operating, and scaling production machine learning platforms, including model serving, monitoring, and large-scale inference workloads.
Experience with MLOps platforms and tools like MLflow, Weights & Biases, KServe, or similar technologies.
Proficiency with containerization, orchestration, and distributed compute environments (Docker, Kubernetes, Slurm, Ray).
Experience operating large-scale AI platforms that deploy, host, and optimize machine learning models for production use, using technologies such as Triton, vLLM, or TensorRT-LLM.
Experience with infrastructure automation and CI/CD practices using tools such as Terraform, Ansible, GitHub Actions, or related.
Experience supporting cloud platforms (AWS, Azure, or GCP) and on-premises GPU infrastructure.
Knowledge of observability and operational monitoring, including metrics, logging, tracing, and performance tuning.
Ability to identify and address system, infrastructure, and model performance issues through automation and continuous improvement.
Ability to collaborate effectively with research scientists, AI engineers, and infrastructure teams in a fast-paced environment.
Your Basic Qualifications
Bachelor’s in Computer Science, Engineering, Statistics, Mathematics, or a related technical field
4 years of experience in machine learning engineering, ML Ops, or platform engineering.

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aimlmachine learningci/cdpythondockerkubernetesllmawsazuregcp

Platform: workday
Effort: long

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Dental

Vision

401(K)