Eli Lilly

Director, Molecular AI & Federated Learning

Eli Lilly • $177K — $281K *
Indianapolis, IN 46227In-Person
Pharmaceuticals & Biotech
5 - 7 years of experience
Today
Be an Early Applicant
Job Overview by Ladders

Qualifications

  • PhD in a relevant computational field (Computer Science, Computational Chemistry)
  • 5+ years of post-PhD experience in machine learning applied to drug discovery
  • Demonstrated technical leadership without formal management experience
  • Proven experience developing generative models for molecular design
  • Strong understanding of medicinal chemistry and ADMET optimization
  • Hands-on experience with federated learning and privacy-preserving ML
  • Publications in top-tier venues related to molecular generation and learning

Responsibilities

  • Set technical direction for federated learning and molecular AI
  • Guide experimental design and review methods as a mentor
  • Architect deep learning architectures for federated pre-training
  • Advance semi-supervised and self-supervised learning methods
  • Develop communication-efficient aggregation strategies for complex models
  • Profile computational performance for federated training and inference
  • Create multi-task models leveraging shared representations

Benefits

  • Comprehensive benefit program including health coverage
  • Participation in a company-sponsored 401(k) and pension
  • Vacation and leave of absence benefits
  • Well-being benefits like employee assistance program and fitness activities
  • Opportunity to attend key industry conferences with minimal travel
Full Job Description
Job Summary

The Director, Molecular AI & Federated Learning is a senior technical leadership role within the TuneLab platform, setting the technical vision that unites privacy-preserving federated learning with generative small-molecule design. This position pairs deep expertise in medicinal chemistry, ADMET prediction, and molecular optimization with advanced capabilities in federated foundation models and multi-task learning, and is responsible for the predictive and generative models that accelerate small-molecule lead optimization and candidate selection across the TuneLab federated network. As a technical director, the role leads through vision, methodological rigor, and mentorship-guiding scientists and shaping research strategy across internal teams and external biotech partners-rather than through formal people management.

Key Responsibilities
  • Technical Vision & Research Strategy: Set the technical direction for federated learning and molecular AI across TuneLab-defining a research agenda that unifies privacy-preserving foundation models, multi-task learning, and generative small-molecule design, and aligning it with platform and portfolio priorities.
  • Technical Leadership & Mentorship: Serve as a principal technical authority and mentor for data scientists and engineers-guiding experimental design, reviewing methods and code, and raising the scientific bar across the team, while influencing technical decisions across disciplines internally and with external partners.
  • Federated Foundation Models: Architect novel deep learning architectures (e.g., Transformer and graph neural network-based) for large-scale federated pre-training on unlabeled or partially labeled data distributed across multiple partner sources.
  • Semi-Supervised & Self-Supervised Learning: Advance state-of-the-art semi-supervised and self-supervised methods (e.g., contrastive learning, masked auto-encoding) tailored to the constraints of federated learning, such as communication bottlenecks and data heterogeneity.
  • Federated Optimization & Aggregation: Develop robust, communication-efficient aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) that remain stable for large, complex models and handle non-IID data across clients.
  • Scalability, Simulation & Performance: Profile and optimize the computational performance-memory, latency, and communication cost-of federated training and inference for scale, and build high-fidelity simulation environments to test, debug, and benchmark federated strategies before real-world deployment.
  • Federated Multi-Task Learning: Architect multi-task learning models that leverage shared representations across related endpoints to improve predictive performance and data efficiency in a federated ecosystem, where each client may hold data for only a subset of tasks.
  • Data & Task Heterogeneity: Design algorithms that address extreme task and feature heterogeneity across clients-personalized models, meta-learning, and gradient-aggregation methods robust to non-IID data-and apply regularization that prevents negative transfer while encouraging positive knowledge sharing.
  • Downstream Adaptation & Validation: Create efficient protocols for fine-tuning and adapting pre-trained federated models to specific downstream tasks, and establish rigorous validation frameworks with appropriate per-task metrics and fairness assessment across clients and tasks.
  • Small Molecule Property Prediction: Build multi-task models for small-molecule properties-including ADMET endpoints, solubility, permeability, metabolic stability, and off-target liabilities-across diverse chemical representations (SMILES, graphs, 3D conformations).
  • Generative Chemistry Models: Design and deploy state-of-the-art generative models (VAEs, diffusion models, flow matching, autoregressive models) for de novo design, lead optimization, and scaffold hopping that respect synthetic accessibility and drug-likeness constraints.
  • ADMET-Driven, Multi-Objective Design: Develop integrated prediction-generation pipelines that optimize molecules simultaneously across multiple ADMET properties while maintaining target potency, using multi-objective optimization and Pareto-front exploration.
  • Chemical Space & Synthetic Feasibility: Implement efficient exploration of synthetically accessible chemical space-reaction-aware generation, retrosynthetic-planning integration, and fragment-based design-collaborating with synthetic chemists to ensure generated molecules are practically synthesizable.
  • Structure-Activity & Representation Learning: Learn and exploit structure-activity relationships from sparse, noisy federated bioactivity data-including matched molecular pair analysis and activity-cliff prediction-and develop self- and semi-supervised molecular representations that generalize to novel chemical series.
  • Interpretability & Scientific Insight: Apply explainability (XAI) techniques to complex multi-task and molecular models to understand predictions and uncover relationships between endpoints, generating novel scientific insight while respecting IP and competitive boundaries across federated partners.
  • Benchmarking, Dissemination & Governance: Establish rigorous benchmarks using public (ChEMBL, ZINC, PubChem) and proprietary Lilly data; author high-impact publications (e.g., NeurIPS, ICML, ICLR) and deliver compelling presentations to internal and external audiences; and uphold reproducible code, internal libraries, and version control for data, code, and models.


Basic Qualifications
  • PhD in Computer Science, Computational Chemistry, Cheminformatics, Machine Learning, Computational Biology, or a related computational field from an accredited college or university
  • 5+ years of post PhD experience applying machine learning to drug discovery within the biopharmaceutical industry or comparable settings or an equivalent record of technical leadership and impact (preference for 8+ years)


Additional Preferences
  • Demonstrated technical leadership-setting research direction, leading complex ML programs, and mentoring scientists-without a requirement for formal people-management experience
  • Proven track record developing generative models for molecular design and multi-task or representation-learning models for complex endpoints
  • Deep understanding of medicinal chemistry principles and ADMET optimization
  • Hands-on experience with federated learning, distributed optimization, and privacy-preserving machine learning
  • Publications in top-tier venues (e.g., NeurIPS, ICML, ICLR) on molecular generation, property prediction, or federated and representation learning
  • Expertise in graph neural networks and geometric deep learning for molecules
  • Strong background in organic chemistry and synthetic-feasibility assessment
  • Experience with fragment-based and structure-based drug design
  • Knowledge of PK/PD modeling and clinical translation
  • Proficiency in cheminformatics tools (RDKit, DeepChem) and modern ML frameworks (e.g., PyTorch)
  • Experience with active learning and design-make-test-analyze cycles
  • Familiarity with uncertainty quantification and explainability (XAI) in federated or multi-task settings
  • Exceptional communication skills, with the ability to understand and navigate complex relationships across disciplines, internally and externally
  • Learning agility and a portfolio mindset-ensuring individual technical decisions align with the overall goals of the TuneLab ecosystem
  • Independent, self-directed, and able to drive ambiguous research problems through to impact


Other Information
  • This role is based at Lilly sites in Indianapolis, San Francisco, or Boston with up to 10% travel (attendance expected at key industry conferences).

Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$177,000 - $281,600

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.

About Eli Lilly

ICOS Corporation is a biotechnology company that engages in the discovery, development, and commercialization of therapeutic products. It is engaged in the commercialization of treatments for unmet medical conditions, such as benign prostatic hyperplasia, hypertension, pulmonary arterial hypertension, cancer, and inflammatory diseases. It is the developer of a treatment known as Cialis (tadalafil), a product for the treatment of erectile dysfunction through its joint venture with Eli Lilly and Company in North America and Europe. It is also engaged in contract manufacturing services for third parties. It is in a strategic alliance with Solvay Pharmaceuticals, Inc. ICOS Corporation was established in 1989, based in Bothell, Washington. It is currently operated by Eli Lilly and Company.

Eli Lilly Careers

Joining Eli Lilly offers an unparalleled opportunity to become part of a leading global team dedicated to creating a healthier future. As a company revered for its commitment to innovation and leadership in the pharmaceutical industry, Eli Lilly is where your professional journey can flourish. Work You’ll Do At Eli Lilly, we are passionate about transforming patient care and advancing medical innovation. Our team at Eli Lilly is at the forefront of developing groundbreaking solutions in healthcare. By joining us, you will collaborate with some of the brightest minds in the industry, using cutting-edge technology to make real-world impacts. Lead with Innovation and Leadership Eli Lilly stands out in the marketplace by integrating deep industry expertise with robust research and development efforts. We are looking for professionals who are eager to drive change and lead the way in developing therapeutic breakthroughs. Explore Job Opportunities and Growth Eli Lilly offers a variety of career paths, including full-time positions and internships, across multiple functions such as research, marketing, IT, and sales. Whether you are a seasoned professional or a recent graduate, Eli Lilly provides an environment that promotes career growth and learning opportunities. Our commitment to diversity and leadership training ensures that every employee can achieve their potential. Be Part of Our Team Our team at Eli Lilly is committed to excellence and driven by a mission to improve lives. Employees enjoy a supportive culture that values collaboration, creativity, and diversity. We believe that a diverse workforce fosters innovation and helps us better connect with the communities we serve. Benefits and Culture Eli Lilly is dedicated to supporting our employees, offering competitive benefits, wellness programs, and comprehensive health care. Our culture is built on a foundation of respect, integrity, and quality, making Eli Lilly not just a great place to work, but a community to grow with. Networking and Professional Development Eli Lilly encourages continuous professional development and networking. With access to various training programs and mentorship opportunities, employees can enhance their skills and advance their careers. Our leadership is committed to nurturing talent through effective training and development strategies. Join Our Team Discover the exciting job opportunities at Eli Lilly by exploring open positions that match your skills and interests. We are continuously hiring and looking for individuals who are passionate, innovative, and ready to contribute to our mission of making life better for people around the globe. Stay Connected Keep up to date with the latest at Eli Lilly by following our careers blog. Gain insights from industry leaders and get tips on everything from crafting the perfect resume to preparing for your interview. Eli Lilly is not just a company—it's a place where you can make a difference. Explore the positions available and find out how your talents can help change the world. SEARCH ELI LILLY JOBS Stay ahead in your career with Eli Lilly, where innovation, leadership, and a commitment to diversity and growth lead the way to future advancements.
Learn more about Eli Lilly
Size
35,000 employees
Market Cap
$344.2 billion
Industry
Pharmaceuticals & Biotech
Net Income
$6.1 billion
Founded
1876
5 Year Trend
+5.9%
Revenue
$24.5 billion
NASDAQ
LLY
* Ladders Estimates

Similar Jobs

Ad banner background

Get Ready For Your
Next Interview

More Jobs at Eli Lilly

More Pharmaceuticals & Biotech Jobs

Find similar Director, Molecular AI & Federated Learning jobs:

NationwideIndianapolis, IN