Chemical Reactivity Landscape, Scientific Project Leader

Job ID: R-111642Company: LillyLocation: San Francisco, California, United States of AmericaJob Type: Full TimeCategory: Research & DevelopmentPosted Date: 2026-09-10At 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. Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley!Making medicine that’s never been made means doing what’s never been done. If you’re an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation, we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us!About the Lilly and NVIDIA PartnershipLilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery’s toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe.Company OverviewAt Lilly, we serve an extraordinary purpose. We make a difference for people around the globe by discovering, developing, and delivering medicines that help them live longer, healthier, more active lives. Not only do we deliver breakthrough medications, but you also can count on us to develop creative solutions to support communities through philanthropy and volunteerism.Organization OverviewThe Lilly Small Molecule Discovery (LSMD) group is an organization purpose-built to create molecules that make life better for people. We focus on using innovative science to unlock novel approaches that can treat people suffering from diseases with poor treatment options. We continually challenge ourselves to deliver molecules that can provide breakthrough efficacy with the highest possible safety margins. We are dedicated to optimizing our mindset, technology, and processes for faster, more nimble execution. Our success is built on a culture that empowers innovative problem solving through open collaboration and individual accountability.Advanced Molecule Design (AMD) is dedicated to optimizing molecules into strong drug candidates that become breakthrough medicines for patients. AMD brings together core functional areas — medicinal chemistry, synthetic chemistry, analytical chemistry, computational chemistry, and molecular pharmacology — to accelerate the identification and advancement of high-quality small molecule candidates across a breadth of modalities and therapeutic areas.Discovery Chemistry Technologies (DCT), a key function within AMD, provides critical scientific expertise and technology solutions that support molecule optimization and discovery chemistry programs. The Chemical Reactivity Landscape sits alongside DCT's Synthetic Innovation Team (SIT), which identifies, integrates, and deploys cutting-edge synthetic chemistry technologies. Where SIT brings modern synthetic methods to the bench, this project builds the predictive layer that tells the organization which conditions to run and why — and the two are designed to operate as one loop. Team members are expected to embrace collaboration, adopt a forward-thinking and growth-oriented mindset, and take individual responsibility for their scientific contributions and professional development.Position SummaryWe are looking for a scientific leader to own the Chemical Reactivity Landscape initiative: a purpose built initiative to build a predictive map of reaction outcomes and generate differentiated reactivity data.This is a hands-on scientific leadership role working at the intersection of quantum chemistry, machine learning, high-throughput experimentation, and laboratory automation. You will set the modeling strategy across mechanistic, quantum chemical, statistical, and learned approaches; direct the synthetic organic core that gives those models something real to learn; and drive the closed loop in which predictions select experiments and experimental results improve predictions. You will also decide how foundation models and agentic systems are used in reaction discovery.The project is run in close partnership with the Synthetic Innovation Team (SIT) in Lilly Small Molecule Discovery (LSMD). SIT identifies, defines, and solves the synthetic bottlenecks that matter to the portfolio and brings new methodologies to the bench.The role is on-site in South San Francisco with both people and project leadership scope.Key ResponsibilitiesScientific Direction & Project LeadershipOwn the scientific vision for the Chemical Reactivity Landscape: working with the Synthetic Innovation Team (SIT) in LSMD, define what the landscape must predict, to what accuracy, for which chemistry, and in what sequence it is builtTranslate scientific ambition into a sequenced plan with dated milestones and decision points, and communicate progress, trade-offs, and risk to senior R&D leadershipSecure and allocate compute, HTE capacity, and headcount against the highest-value scientific questionsReactivity Science & ModelingPartner with computational chemistry, jointly defining the level of theory, conformational sampling, transition-state treatment, and solvation strategy suited to the scientific question at handDrive descriptor and representation strategy so that models generalize across chemotypes and reaction classes, and so that their predictions can be explained in chemical termsLead catalysis-and other reaction discovery and optimization efforts defining the substrate and condition spaces worth searchingMaintain deep command of the primary literature across organic chemistry, machine learning, and computational chemistry, quickly judging which advances are worth bringing insideAutonomous Experimentation & Closed-Loop DiscoverySet the direction for integrating foundation models and agentic systems into reaction discovery, agents that plan calculations, propose conditions, dispatch experiments to automated and cloud laboratories, and interpret the readouts that come backDrive the closed loop between prediction and experiment so that HTE campaigns are selected by the model and their results measurably improve itChampion low-friction and conversational interfaces that put reactivity models in the hands of bench chemists without requiring them to become modelersPartner with automation, HTE, and analytical leadership so that experiments produce structured, model-ready, traceable data by default rather than by retrofitEvaluation, Rigor & Scientific StandardsEstablish how models and agents are evaluated before they influence experiments: benchmarks, held-out data, fair expert and random baselines, calibrated uncertainty, and documented failure modesTreat scientific evaluation of foundation models as a first-class discipline rather than a post-hoc check, and build the harnesses and reference datasets that make it routineEstablish reproducibility, data provenance, and clear reporting of each model’s domain of applicabilityTeam Leadership & DevelopmentLead, manage, and develop a team of computational and data scientists across career stages, including the Reaction Informatics individual contributors on this project: set clear expectations, give direct feedback, and create the conditions for high performanceLead the matrixed cross-functional project team through scientific credibility and clarity of direction rather than authorityMentor and coach scientists at all levels, and give people genuine scientific territory to own and grow intoRecruit, hire, and onboard across career stages, and invest in early-career scientists as part of the long-term talent pipeline for reactivity modeling at LillyActively build inclusive team relationships and ensure all team members have appropriate access to challenging work, development opportunities, and visibilityFoster digital fluency beyond the immediate team: encourage experimentation with new tools, support training opportunities, remove barriers to adoption, and recognize scientists who use these capabilities to accelerate real chemistryCollaboration, Influence & External EngagementPartner with the Synthetic Innovation Team, medicinal chemistry, process chemistry, analytical sciences, and automation to turn portfolio bottlenecks into well-posed scientific questionsEngage LSMD project teams directly to understand their synthetic bottlenecks, and bring an enterprise mindset — proactively connecting teams to reactivity predictions, AI/ML-assisted retrosynthesis, and emerging technologies beyond the immediate scope of any one program.Represent the project in internal governance bodies, scientific forums, and cross-functional decision-makingBuild and manage external collaborations with academic groups, consortia, and technology partners, and evaluate them for strategic fit rather than noveltyPublish and present in leading chemistry, cheminformatics, and machine learning venues, and use that visibility to attract talent and partnersSafety, Quality & ComplianceKeep safety as the top priority; provide guidance on HSE related to new technology deployment, and laboratory operationsEnsure the team maintains rigorous documentation, data integrity, and compliance standards across all computational and experimental workWhat Success Looks LikeThe reactivity landscape is the default reference for condition selection across LSMD, and its uncertainty estimates hold up under real useOptimization campaigns reach target conditions in measurably fewer experiments than expert-designed baselines, across multiple reaction classesModels generalize to out of domain chemistry, and know precisely where they stop workingAutonomous and semi-autonomous campaigns run reliably against real instruments, with the loop closing without manual interventionEvaluation standards set by this project are adopted by other AI-for-science efforts across LillyBasic QualificationsPh.D. in Chemistry or a closely related field with 10+ years of relevant experience; or M.S./B.S. with 15+ years of relevant industry experienceDeep expertise in synthetic and HTE chemistry applied to organic reactivity — mechanism, and physical organic principles — with a track record of results that changed experimental decisionsDemonstrated expertise applying machine learning to chemical data, including reaction outcome, reaction condition, or molecular property predictionDemonstrated experience leading, managing, and developing scientists across multiple career stagesTrack record of owning a scientific capability or platform end to end: setting direction, deploying it to real users, and iterating based on adoption and impactAdditional Skills / PreferencesStrong record of scientific contribution including peer-reviewed publications, presentations, and/or patentsExperience integrating foundation models or agentic systems into scientific workflows, including autonomous or semi-autonomous experimentationExperience with automated and cloud laboratories, HTE platforms, and closed-loop optimization at scaleDepth in catalysis — metal-mediated, organocatalytic, or photoredox — and in catalyst and ligand designExperience establishing evaluation and benchmarking methodology for models or agents, including safety review and failure-mode analysisExperience with Bayesian optimization, active learning, or LLM-guided optimization for reaction conditionsExperience building and leading a scientific function or group from an early stageExperience managing academic collaborations, consortia, or external technology partnershipsExperience contributing to scientific standards, governance, or advisory bodies for AI in scienceFamiliarity with medicinal chemistry principles and an understanding of scale-up considerations.Proficiency with scientific and informatics tools such as ChemDraw, SciFinder, Reaxys, electronic lab notebooks, and data visualization platforms.Examples of championing new technology adoption within a team or organization.Strong work ethic with proactive, independent initiative; anticipates challenges and proposes solutions.Highly organized with strong time management skills; adapts to changing business needs and timelines to deliver timely results.Builds positive, respectful working relationships across teams, and engages in constructive dialogue even when navigating differing viewpoints.Communicates clearly, thoughtfully, and professionally, adapting style to diverse audiences; actively listens and seeks to understand before responding.Views personal success in the context of team success.Proven ability to work effectively in a multidisciplinary, multi-site, international team environmentAdditional InformationTravel: 0–15%. This is a primarily computational, lab-adjacent role with scientific leadership scope. You will work alongside laboratory teams and may spend time in research laboratory spaces to observe and direct experimental workstreams; standard personal protective equipment is required in those settings. The majority of the work is performed in an office and computing environment.This job description is intended to provide a general overview of the job requirements at the time it was prepared. Job requirements may change over time and may include additional responsibilities not specifically described in the job description. Consult with your supervisor regarding actual job responsibilities and any related duties that may be required for the position.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 () 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 (View email address on click.appcast.io), 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$177,000 - $308,000Full-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.#WeAreLillySkillsmachine learningsynthetic chemistryquantum chemistryhigh-throughput experimentationlaboratory automationcomputational chemistrycatalysisreactivity modelingexperimental designdata visualizationchemdrawscifinderreaxyselectronic lab notebooksscientific publishingscientific leadershipteam managementcross-functional collaborationscientific evaluationbenchmarkingactive learningbayesian optimizationcollaboration with technology partnersscientific standards developmentai for chemistrymentoringrecruitment