Advisor – Agent Research

Eli Lilly and Company

. Partner with scientists to build autonomous agents for molecule discovery tasks .

Posted 10/15/2026full-timeUnited StatesJunior💰 $151,500 - $222,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in building and integrating autonomous agents for molecule discovery using reinforcement learning and AI/ML techniques. Proficient in Python and deep learning frameworks, with a strong background in bioinformatics and cheminformatics.

Highest-signal resume keywords

PhD In Machine Learning, Bioinformatics, Cheminformatics, Or Computer ScienceProficiency In PythonExperience With ML/Deep Learning Frameworks Such As PyTorch, TensorFlow, JAXHands-On Experience Training Or Post-Training AI ModelsFamiliarity With Molecular Representation Learning And Generative Chemistry

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningReward ModelingPost-Training MethodsAgentic AI SystemsEnd-To-End System DesignCloud EnvironmentsMolecular Graph MLEvaluation InfrastructureExperiment TrackingData-Driven Decision Making

Soft Skills

CollaborationMentoringCommunicationResearch ExperienceProject Contributions

Tools & Technologies

AWSAzureNextflowArgoKubernetesMLflowRDKitTRLVerlHuggingFace

Industry Keywords

Molecule DiscoveryChemistryBiologyNeuroscienceAI ResearchScientific SignalsDMTACloud-Native Pipeline ArchitecturesEmerging TrendsPublications In ML/NLP Venues

Tech Stack

Tools & technologies

AWSAzureCloudKubernetesPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Partner with scientists to build autonomous agents for molecule discovery tasks
Design and build reinforcement learning environments for real discovery tasks with appropriate state, action, and termination semantics
Curate and engineer reward functions from noisy scientific signals
Post-train domain models using SFT, DPO/GRPO/PPO, reward modeling, and distillation on chemistry and biology tasks
Integrate learned policies with RDKit, molecular graph ML, ELN/LIMS APIs, and instrument drivers so models execute real DMTA tasks
Build evaluation infrastructure including task suites, scoring harnesses, regression tracking, and experiment tracking such as MLflow
Represent Frontier AI in the broader AI@Lilly and external AI research community
Publish, give talks, review papers, and scout emerging trends
Evaluate external vendors, open-source projects, and academic collaborations for strategic fit
Develop models and infrastructure that improve agent performance, reduce DMTA turnaround, and transition prototypes to production-deployed AI systems

Requirements

What you’ll need

PhD, or MS + 3 years / BS + 5 years equivalent experience, in Machine Learning, Bioinformatics, Cheminformatics, Computer Science, or related STEM field
Demonstrated wet-lab collaboration or hands-on experience
Approximately 1–2 years of demonstrated experience applying AI/ML in biology, chemistry, neuroscience, or a related field; industry postdoc counts
Hands-on experience training or post-training AI models
Proficiency in Python
Deep experience with ML/deep learning frameworks such as PyTorch, TensorFlow, JAX, and HuggingFace
Experience with reinforcement learning and post-training methods including PPO, GRPO, DPO, reward modeling, RLHF/RLAIF, and libraries such as TRL or verl
Familiarity with molecular representation learning, generative chemistry, or protein/nucleic acid models
Hands-on experience building agentic AI systems
Experience designing and shipping end-to-end systems in cloud environments
Working knowledge of AWS/Azure cloud-native pipeline architectures, including Nextflow and Argo on Kubernetes
Demonstrable research experience through project contributions; publications in relevant ML/NLP venues are ideally preferred
Experience mentoring and guiding junior researchers or engineers

Benefits

Comp & perks

Company bonus depending, in part, on company and individual performance
Company-sponsored 401(k)
Pension
Vacation benefits
Medical, dental, vision and prescription drug benefits
Healthcare and/or dependent day care flexible spending accounts
Life insurance and death benefits
Time off and leave of absence benefits
Well-being benefits, including employee assistance program, fitness benefits, and employee clubs and activities
Employee resource groups and support networks