Postdoctoral Fellow - Agentic AI Solutions and SciML
Eli Lilly and Company · Indianapolis, IN · 1 mo ago
Analyst$58k–$123k/yrFull-time
We need to output HTML fragment with sections: headings .
Requirements: Basic Qualifications bullet list.
Additional Preferences: Could be under Qualifications or Skills. The posting has "Additional Preferences" list. We'll treat as Qualifications (or Skills). Since we have separate sections for Requirements and Qualifications, we can put Basic Qualifications under Requirements, and Additional Preferences under Qualifications.
Skills: Could be derived from preferences but maybe we can put them under Skills. However we need to avoid thin sections. Might combine Additional Preferences into Qualifications.
Benefits: The posting mentions benefits: "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)." Also mentions company bonus. So we can include Benefits section.
Pay: The posting includes "Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is $58,000 - $123,200 Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance)." So we have Pay section.
Schedule: Not explicitly mentioned; maybe full-time implied but not stated. No schedule details. So we should not create Schedule section.
We must not include boilerplate like EEO, accommodation request, etc.
We must not repeat job title as heading.
We must not include duplicated lines.
We need to output only HTML fragment.
Let's craft:
. The instruction: Use
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Remove boilerplate, EEO, etc. Keep substantive info. We need to parse the posting. Key parts: - Opening paragraph about Lilly, mission, etc. Could be "About the role"? Actually "About the role" maybe includes overview of organization and position overview. We'll create sections: About the role (or Organization Overview?), Responsibilities, Requirements (Basic Qualifications), Qualifications (Additional Preferences?), Skills (maybe combine), Benefits, Pay, Schedule. We need to preserve all substantive info, not invent. Let's extract: About the role: Could include organization overview, position overview. The posting includes "Organization Overview Delivery, Devices, and Connected Solutions (DDCS) sits within Eli Lilly's Product Research & Development organization..." and "Position Overview: The Postdoctoral Fellow, Agentic AI Solutions and SciML will partner..." So we can put that under About the role. Responsibilities: The "Key Responsibilities" section with bullet points (multiple paragraphs). We'll convert each bullet to
About the role
... (combined organization overview and position overview)
ThenResponsibilities
- ... each bullet
Requirements
- ... basic qualifications bullets
Qualifications
- ... additional preferences bullets
Benefits
... benefits description
ThenPay
... compensation range and bonus
We need to ensure we preserve all substantive info. Let's extract the About the role content: From posting: "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 Delivery, Devices, and Connected Solutions (DDCS) sits within Eli Lilly's Product Research & Development organization. We are a diverse team of scientists and engineers responsible for discovering, designing, and developing patient-centric drug delivery solutions across a broad range of modalities — from injection devices to novel routes of administration and nanomedicines. DDCS drives the drug delivery innovation agenda across early and late development to meet the needs of an expanding portfolio that spans small molecules, biologics, and nucleic acid therapeutics. DDCS is organized around a matrix model with strong disciplinary and functional horizontals supporting innovation and commercialization verticals. Our vision is to get our medicines to more patients faster by accelerating reach and scale, guided by three strategic pillars: Delivery Systems, Robust & Sustainable, and Patient Experience + Outcomes. The Digital Transformation and Data Science team within DDCS serves as a key foundation for DDCS's digital transformation efforts. The team helps make data work more effectively for the organization by turning information into faster insights, stronger decision-making, improved ways of working, and practical AI solutions embedded in everyday DDCS workflows. Position Overview: The Postdoctoral Fellow, Agentic AI Solutions and SciML will partner with DDCS business functions to help translate machine learning, statistics, scientific computing, and AI concepts into practical tools that improve speed, productivity, and impact across the organization. Operating within the Digital Transformation and Data Science team, this role will assist in the design, build, and deployment of AI-enabled workflows, agentic scientific systems, knowledge extraction tools, and scientific ML capabilities into actionable decisions." We'll combine that into a paragraph or multiple paragraphs. Usefor prose. Could split into two paragraphs: first about Lilly mission, second about organization overview and position overview. But we can keep as one paragraph; it's long but okay. Now Responsibilities: The posting lists under "Key Responsibilities" several bullet-like paragraphs each starting with a bold heading? Actually they are sections: "AI Solutions Engineering & Practical Tool Delivery", "Agentic Scientific AI Systems & Knowledge Extraction", "Data Strategy, Decision Support & Workflow Transformation", "Reliability, Validation, MLOps & Responsible AI", "Cross-Functional Collaboration & Scientific Translation", "Capability Building, External Leadership & Mentorship". Each has multiple sentences. We need to turn each bullet into
- for any list of duties/requirements/benefits. So we should treat each of these sections as a list item. Within each item we can keep the sentences as prose.
Thus:
- Partner with business functions across DDCS to identify, prioritize, and scope high-value opportunities where AI, machine learning, and automation can improve speed, productivity, insight generation, and decision quality. Translate stakeholder needs into practical AI tools, technical designs, acceptance criteria, and delivery plans that fit real scientific, engineering, and operational workflows. Develop AI-enabled applications, services, and workflows that integrate models, data sources, document collections, and user-facing interfaces for decision support and workflow automation.
- Create reusable scientific agent skills, task harnesses, validators, run ledgers, and reproducibility controls that allow AI agents to execute diverse, long-running tasks reliably. Build agentic knowledge extraction and question-answering systems for structured and unstructured technical content, including PDFs, Word documents, handwritten notes, design histories, experimental records, and regulatory-relevant evidence. Design evaluation, monitoring, guardrails, and human-in-the-loop escalation patterns so agentic outputs are auditable, traceable, and appropriate for high-consequence technical decisions. Apply knowledge graphs, data ontologies, and structured knowledge representation where they improve retrieval, traceability, and reuse.
- Contribute to DDCS data and AI strategy by identifying reusable patterns, data needs, platform capabilities, and solution architectures that support digital transformation at scale. Turn information from experiments, simulations, development documents, and business processes into faster insights, stronger judgment, and improved ways of working across innovation and commercialization efforts. Communicate model predictions, evidence, assumptions, limitations, uncertainty, and recommended actions through clear visualizations, decision-support outputs, and quantitative business cases that influence solution adoption, workflow redesign, platform investments, and portfolio priorities.
- Champion software engineering best practices including version control, automated testing, CI/CD, containers, documentation, reproducibility, observability, and fit-for-purpose MLOps/agent-ops practices. Develop validation, monitoring, documentation, and model-risk approaches aligned with intended use, responsible AI principles, GxP awareness, and regulatory expectations where applicable. Leverage cloud infrastructure (and HPC/GPU resources where needed) to develop, test, deploy, and scale agentic workflows, document intelligence systems, and analytics applications.
- Partner across the DDCS matrix with drug delivery scientists, device engineers, formulation scientists, data scientists, AI application engineers, quality, clinical, regulatory, and business stakeholders. Identify and prioritize high-impact opportunities where AI solutions, scientific ML, agentic workflows, or knowledge extraction can reduce development time, improve productivity, or mitigate technical and business risks. Translate complex analytical and AI findings into clear narratives and quantitative business cases that influence solution adoption, workflow redesign, platform investments, and portfolio priorities.
- Advance the DDCS technology roadmap for practical AI tools, document intelligence, agentic workflows, and reusable knowledge systems. Share methods, reference patterns, and lessons learned that help DDCS embed data and AI into everyday work across scientific and business functions. Mentor team members and partners on reliable agentic systems, responsible AI, and rigorous communication of model assumptions, uncertainty, and decision impact. Stay current with the fast-moving agentic AI and LLM landscape and bring new tools, frameworks, and techniques into DDCS’s practice where they add real value.