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About This Role
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.
The Copay Contract Administrator plays an integral role in helping Lilly become our customers' preferred company, preserving shareholder health, and providing exceptional service to patients and partners through timely, compliant processing of patient affordability programs. This role's primary focus is copay payment processing — including copay cards and vouchers — with regular assistance on Commercial and Part D rebate payments and Revenue Defender (RD), Lilly's AI/anomaly detection program. The ideal candidate is analytical, detail-oriented, and comfortable moving between copay operations, traditional rebate payment work, and AI-enabled analytics — someone who is service-oriented, adaptable, and accountable in delivering timely, accurate, contract-compliant payments across both program areas.
Summary of Job Responsibilities
Knowledge & Expertise
- Develop deep knowledge of patient affordability program designs, Contract Administration procedures and systems, contract requirements, and product details.
- Build working knowledge of Commercial and Part D rebate contracting and payment processing to support cross-coverage on the broader CA team.
- Develop a working understanding of AI/ML tools used in Revenue Defender investigation and their application to copay and rebate operations.
Relationship Management
- Establish broad and deep relationships with key internal partners such as Channel Management, Legal, Lilly USA Finance, Lilly Patient Services, IDS, and the Patient Affordability Center of Excellence.
- Partner with suppliers managing Lilly's patient affordability programs, proactively identifying potential invoicing issues and recommending mitigating controls.
- Build supporting relationships with Contract Development, Gross-to-Net, and IT as needed to support rebate payment assistance.
Copay & Patient Affordability Payment Processing
- Proactively ensure receipt of invoices and related details from external third parties and analyze for accuracy and contract compliance.
- Uphold internal controls, including performing peer review of documentation support.
- Participate in the development and review of key contractual language to minimize risk and identify potential gaps, collaborating with stakeholders to resolve issues.
- Support audit and business monitoring activities related to copay and patient affordability programs.
Rebate Payment Assistance
- Assist with Commercial and Part D rebate payment processing as needed, including supporting reconciliation of payment requests vs. actual payments and investigating trend variances.
- Help route payments for FRAP approval/signature and support meeting customer payment deadlines during peak periods or team capacity gaps.
Revenue Defender (RD) Support
- Assist Revenue Defender initiatives by helping identify ineligible submissions, compliance exceptions, and potential revenue leakage across copay and rebate data sets.
- Document findings and collaborate with the Revenue Defender team on recoupment and corrective action as needed.
Continuous Improvement, Analytics & Automation
- Analyze data to discover new insights and drive business solutions across copay and rebate operations.
- Partner with IT to maintain and improve payment processing and validation tools.
- Identify opportunities for process standardization, automation, and AI-enabled analytics, and support implementation of improvements.
- Respond to ad-hoc needs of organizational leadership and other business partners, including support for Medicaid and 340B efforts as needed.
Role Flexibility
- This role is expected to operate with flexibility across Contract Administration's core work areas. While the primary focus is copay payments, team members may be asked to support additional areas, including Commercial and Part D rebate payments, and Revenue Defender, based on evolving business needs, team capacity, and organizational priorities.
Basic Qualifications
- Bachelor's degree
- 1 year of work experience in pharma rebates, PBM, healthcare finance, or data analytics. Alternatively, recent graduates with internship experience and/or coursework in a quantitative field may be considered.
- Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1
Preferences
- Preferred degree in (e.g., Business, Finance, Economics, Data Analytics, Management Information Systems, Information Technology) or an analytical degree emphasizing critical thinking and problem-solving.
- Work experience or coursework that clearly demonstrates analytical capabilities.
- History of teamwork and customer service skills.
- Experience with data flow, internal controls and processes, and system connectivity.
- Demonstrated attention to detail.
- Familiarity with AI-enabled analytics and automation tools (e.g., Revenue Defender) to accelerate data validation, anomaly detection, and trend analysis is a plus.
- Knowledge of pharmacy claim data and how it flows through related systems.
- Working knowledge of SQL for querying and analyzing rebate and claims data in a data warehouse environment (e.g., Redshift, SQL Server).
- Advanced Excel skills, including pivot tables, Power Query, and formula-based reconciliation of large data sets.
- Experience with Power BI or a similar business intelligence/visualization tool for trend reporting and dashboarding.
- Exposure to automation or scripting tools (e.g., Power Automate, Python) to support process standardization and efficiency.
- Experience with a rebate management platform (e.g., ICyte) and familiarity with pharmacy claims data structures (NDC, NCPDP) preferred.
- Ability to identify data anomalies or patterns across large transactional data sets to support revenue protection and compliance.
Additional Information
- Travel Percentage: 0–10%
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 (https://careers.lilly.com/us/en/workplace-accommodation) 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
$60,000 - $143,000
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.
#WeAreLilly
Salary Context
This $60K-$143K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Eli Lilly, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($101K) sits 52% below the category median. Disclosed range: $60K to $143K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Eli Lilly AI Hiring
Eli Lilly has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US. Compensation range: $143K - $246K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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