Interested in this AI/ML Engineer role at Eli Lilly?
Apply Now →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.
Organization Overview:
At Eli Lilly and Company, we unite caring with discovery to make life better for people around the world. Lilly has led global healthcare for more than 140 years, and today we're growing faster than ever, expanding our portfolio, modalities and manufacturing footprint. To keep delivering medicines faster, with greater quality and efficiency, we're investing in the next generation of digital and AI capability that will change how we research, develop and make decisions.
Position Overview:
We're looking for a visionary executive to lead the next paradigm shift in pharmaceutical development: agentic AI. This is the shift from human-executed workflows supported by software to autonomous, AI-enabled development ecosystems, where scientists and engineers spend their time on creativity, judgment and strategy while intelligent agents handle routine knowledge work, analysis, documentation, planning and execution.
This Associate Vice President will define and execute Lilly's vision for Agentic CMC Development: building a pan-DDCS/PRD AI capability platform and deploying agentic solutions that transform how development work gets done across drug delivery, devices, combination products, synthetic molecule development, biologics, formulation sciences, process development, analytical sciences, quality, regulatory and the manufacturing interface.
The long-term goal is an Autonomous Development Platform: one that continuously learns from enterprise knowledge, experimental outcomes, development history, manufacturing performance, regulatory interactions and scientific literature, and increasingly executes development work on its own. This platform will enable:
- Agent-driven knowledge discovery and technical intelligence
- Autonomous technical workflow orchestration
- AI-generated development strategies and experiment plans
- Automated documentation and regulatory content generation
- AI-assisted design, optimization, change control planning and risk assessment
- Digital experts and copilots embedded across development functions
- Enterprise-scale scientific knowledge management
- Agentic coordination between development, manufacturing, quality and regulatory functions
Key Responsibilities:
This is a leadership role that builds and grows a dedicated agentic AI team, establishes the DDCS/PRD wide capability platform, and integrates it seamlessly into all processes of CMC development. As the functional technical leader, you partner with scientists, engineers and senior leaders across DDCS/PRD, Manufacturing, Discovery, Business Units, and corporate AI leadership to turn this vision into production reality. You'll serve as a CMC champion for agentic AI in development sciences, setting direction, building durable platform assets, and proving measurable gains in speed, quality and productivity across the CMC development lifecycle.
- Set the Agentic AI Strategy and Roadmap
- Serve as the single point of accountability for DDCS and pan PRD agentic AI strategy, setting a multi-year vision, roadmap and technology strategy for autonomous development systems.
- Define the future-state operating model for an AI-enabled development organization and prioritize the highest-value opportunities for transformation.
- Build the business case and champion narrative that win support from scientists, engineers and senior leaders, addressing both the opportunity and the risks of agentic AI openly and directly.
- Track Lilly's growing portfolio of agentic AI initiatives and external pharma agentic AI moves, translating industry and competitive trends into roadmap decisions.
- Own the Agentic AI Platform Architecture
- Lead development of an enterprise-scale platform spanning multi-agent orchestration frameworks, large language model ecosystems, agent memory architectures, retrieval-augmented generation and knowledge graph systems, digital twin and simulation integration, and autonomous workflow engines.
- Establish standards for agent-to-agent and tool communication (for example, Model Context Protocol and Agent-to-Agent standards) so agents work together across the pan PRD data and knowledge landscape.
- Build reusable enterprise capabilities that DDCS, BRD, SMDD and Manufacturing can all adopt.
- Build in responsible AI controls from the start: human-in-the-loop quality systems, AI governance and compliance frameworks, and GxP/21 CFR Part 11 auditability for every agent output that touches a regulatory submission.
- Build and Lead the Agentic AI Development Team
- Recruit, build and lead a world-class team of AI scientists, AI application engineers, software architects, product managers, workflow transformation leaders and domain experts.
- Recruit and develop top talent from the pharmaceutical, technology, AI and startup ecosystems, building an organization recognized as an industry leader in applying agentic AI to scientific and engineering development.
- Foster a culture of experimentation, innovation, speed and continuous learning.
- Talent Magnet and Builder: Attract, develop and retain exceptional technical and business talent while building future leadership pipelines.
- Lead Agentic Solution Development End-to-End
- Lead deployment of intelligent agent ecosystems across CMC development workflows, including scientific knowledge discovery and synthesis, literature mining and competitive intelligence, experiment planning and optimization, formulation design support, process development optimization, design, technical report authoring, regulatory submission content generation, and quality investigations.
- Develop full-stack applications that integrate large language models, retrieval-augmented generation and agentic workflows with intuitive front ends and secure back-end services, automating today's manual CMC development data and documentation tasks.
- Drive the transition from manual, document-centric workflows to autonomous, AI-native development processes, monitoring and evaluation practices that keep agents accurate, fast and cost-effective after launch.
- Orchestrate Cross-Functional Delivery with CMC development partners
- Serve as the bridge between DDCS/PRD scientists, engineers, quality, manufacturing and regulatory colleagues and the technical team, translating business needs into technical requirements and technical capability into business value.
- Partner with corporate AI, digital and business leadership to align DDCS and PRD initiatives with Lilly's broader AI strategy, driving adoption of agentic operating models, enterprise copilots and next-generation digital worker concepts.
- Coordinate with Tech@Lilly and Enterprise IT so agentic AI work stays aligned with enterprise platforms and standards rather than running as a parallel effort.
- Own the Business Case, ROI and Success Metrics
- Define and track efficiency, cycle-time, quality and adoption metrics that show agentic AI's impact on CMC development speed and output.
- Own the ROI narrative for agentic AI investment in DDCS/PRD, reporting measurable reductions in development cycle time and technical workload to senior leadership.
- Build durable, reusable automation assets, not one-off pilots, so gains compound across programs and years.
- Drive Adoption and Change Management
- Lead change management for new agentic AI capabilities: stakeholder engagement, communications, training and deployment support so agents get embedded in real workflows, not just piloted.
- Establish feedback loops with scientists and engineers to identify adoption barriers, prioritize enhancements and build a culture of responsible AI adoption across DDCS.
- Free scientist and engineer time from repetitive, document-heavy work so they can focus on the creativity- and judgment-based work that drives real innovation.
- Represent Lilly's agentic AI-for-CMC agenda in industry consortia, regulatory forums and academic partnerships, and build strategic relationships with leading AI companies and technology providers that bring outside perspective into DDCS.
- Organizational Transformation: Lead transformational change across complex, matrixed organizations while delivering measurable business outcomes.
- Scientific leadership in Lilly and pharma industry
- Create breakthrough strategies that redefine how development work is performed and position Lilly for sustained competitive advantage, building Lilly's reputation as a leader in agentic AI-enabled drug delivery and development.
- Advance disruptive technologies and operating models that meaningfully improve portfolio performance and enterprise productivity.
- Influence Beyond Authority: Align diverse stakeholders across organizations, geographies and disciplines toward a common vision. Build and use a diverse external network across industry, academia and regulatory bodies to bring outside perspective and speed breakthrough thinking inside DDCS.
- External Impact: Shape industry practices, build strategic partnerships, and establish Lilly as a recognized leader in agentic AI-enabled pharmaceutical development.
Basic Requirements:
- PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Pharmaceutical Sciences or a related field
- 15+ years of progressive experience leading technology, digital transformation, AI or scientific organizations.
- Proven track record leading enterprise-wide transformational initiatives and building or scaling high-performing technical organizations. Experience applying AI, digital transformation or automation technologies for pharmaceutical, biotechnology, healthcare or manufacturing environments.
- Demonstrated experience deploying production AI systems at enterprise scale, with a strong understanding of agentic AI architectures, autonomous systems, LLM platforms, retrieval systems and orchestration frameworks.
- Demonstrated executive leadership experience influencing senior corporate stakeholders, with the ability to translate complex technical visions into business terms and organizational outcomes.
- Expertise in full-stack AI application design from prototype to production, agent-based systems, autonomous workflows, multi-agent architectures and AI-driven process transformation, including standards such as Model Context Protocol and Agent-to-Agent.
Additional Preferences:
- Experience building AI platforms that serve large, matrixed organizations.
- Familiarity with regulated environments, GxP systems, quality systems and regulatory expectations, including 21 CFR Part 11.
- Knowledge of CMC development, pharmaceutical development, manufacturing sciences or medical device development.
- Experience integrating AI with modeling and simulation, digital twins, laboratory systems and enterprise data platforms.
- Strong external reputation within AI, digital transformation, scientific computing or pharmaceutical innovation communities.
- Enterprise platform thinking: reusable architectures, standards and durable capability over one-off pilots
- People leadership: recruiting, building and growing a high-performing, multidisciplinary technical team
- Responsible AI: governance, compliance and model risk management in regulated environments
- Clear, confident communication of AI capability and business value to diverse audiences
- Systems thinking across data, models, applications, workflows and people
Other Information:
- Travel: 10–20%
- Location: Indianapolis, IN; Lilly Technology Center – North (LTC-N)
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
$219,000 - $352,000
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 $219K-$352K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($285K) sits 35% above the category median. Disclosed range: $219K to $352K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Eli Lilly AI Hiring
Eli Lilly has 7 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Indianapolis, IN, US, IN, US, San Diego, CA, US. Compensation range: $173K - $391K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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