Scientific Lead - Forward Deployed AI Engineer, Applied Intelligence for Discovery

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.

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