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About This Role
At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life\-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.
The Opportunity
We are building something unprecedented — an AI foundation that will push the frontier on what is possible today across drug discovery research, from target identification and disease biology through translational science.
The Applied Intelligence for Discovery (AI4D) team is a newly formed group within Lilly Research Laboratories that operates at the intersection of scientific delivery and core platform development. AI4D’s mission is to connect scientists to petabyte\-scale data through natural language interfaces, automated analysis workflows, and intelligent search — and to convert early deployments into repeatable system standards and evaluation practices that scale across therapeutic areas.
The Forward Deployed AI Engineer is the connective tissue between what AI can do and what discovery scientists need it to do. You will embed directly with research teams across therapeutic areas translating real\-world data, infrastructure, and scientific constraints into production systems that collapse the time from question to answer from days or weeks to minutes. You will measure success through production adoption, measurable workflow impact, and eval\-driven feedback loops that inform platform and model roadmaps. This is not a traditional software engineering or data science role. You will sit with biologists, geneticists, and computational scientists working with petabytes of multi\-omics data, leading end\-to\-end deployments from scoping through sustained production use.
Key Responsibilities
- Embed with computational biology and disease biology teams in your assigned therapeutic area to develop deep understanding of their workflows, data, tools, and bottlenecks
- Translate use\-cases into concrete, testable prototypes with clear success criteria. Rapidly turn ideas into a working demo, complete with evaluation benchmarks that tighten acceptance criteria over time
- Design and ship production systems quickly that solve specific scientific problems; owning integrations, data provenance, reliability, and on\-call readiness
- Apply LLM, retrieval\-augmented generation (RAG), text\-to\-SQL, agentic AI frameworks, and other emerging approaches to drug discovery challenges including target identification, biomarker prioritization, mechanism of action studies, and extraction of insight from large\-scale multi\-omics datasets
- Run evaluation loops that measure model and system quality against workflow\-specific scientific benchmarks; use results to drive model selection, product changes, and iterative evidence generation that tightens acceptance criteria over time
- Distill deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses that scale across therapeutic areas and accelerate future development
- Partner closely with AI/LLMOps engineers on the AI4D team to ensure your field\-tested solutions feed back into the platform as reusable components, not one\-off builds
- Contribute to a culture of experimentation, speed, and evidence\-based impact measurement within the AI4D group and the broader LRL research community
Basic Qualifications
- PhD in computational biology, bioinformatics, data science, computer science, or a related field, with 3\+ years of software/ML engineering or technical deployment experience; or equivalent demonstrated experience building and deploying AI/ML tools for scientific applications in biotech, pharma, or scientific software; MSin computational biology, bioinformatics, data science, computer science, or a related field, with 5\+ years of software/ML engineering or technical deployment experience; or equivalent demonstrated experience building and deploying AI/ML tools for scientific applications in biotech, pharma, or scientific software
Additional Skills/Preferences
- Strong programming skills in Python and familiarity with the modern AI/ML ecosystem, including experience with LLMs (API usage, prompt engineering, fine\-tuning), and common frameworks (PyTorch, HuggingFace, LangChain/LlamaIndex, or similar)
- Have owned AI deployments end\-to\-end from scoping through production adoption, and improved them through evaluation design, error analysis, and iterative evidence generation
- Sufficient biological knowledge to have productive conversations with computational scientists and understand the research context behind their problems; prior experience working with multi\-omics data (RNA\-seq, proteomics, GWAS, spatial transcriptomics, or similar) is strongly preferred
- Experience building data\-driven applications including interactive dashboards, natural language interfaces, or automated analysis pipelines
- Communicate clearly across scientific, computational, technical, and executive audiences, translating technical tradeoffs into decision quality and measurable outcomes; you build trust with scientists who have deep domain expertise and make complex technology approachable without being condescending
- Familiarity with cloud computing environments (AWS preferred) and version control (Git)
- Experience in pharmaceutical, biotech, or life sciences R\&D environments
- Familiarity with agentic AI frameworks and building AI\-powered workflows that chain multiple models or tools together
- Experience with biological foundation models (e.g., scGPT, Geneformer for single\-cell; ESM for proteins; AlphaFold) or their application to research problems
- Knowledge of biomedical ontologies, knowledge graphs, or experience integrating heterogeneous biological data sources
- Track record of driving adoption of technical tools among non\-engineering users
- Contributions to open\-source projects or a public portfolio of applied AI work
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 Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ\+ Allies), Veterans Leadership Network (VLN), Women’s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.
Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is
$166,500 \- $266,200
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 $166K-$266K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($216K) sits 17% above the category median. Disclosed range: $166K to $266K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Eli Lilly AI Hiring
Eli Lilly has 4 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Positions span San Francisco, CA, US, Indianapolis, IN, US. Compensation range: $165K - $283K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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