Advisor – Agent Research

Eli Lilly and Company

Scientist-engineer developing reinforcement-learning agents and evaluation systems for Eli Lilly’s autonomous molecule discovery platform. Integrating AI models with chemistry, biology, laboratory, and cloud tools.

Posted 8/11/2026full-timeSan Francisco • California, Massachusetts • 🇺🇸 United StatesJunior💰 $151,500 - $244,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in building and integrating autonomous agents for scientific discovery using reinforcement learning and machine learning frameworks. Proficient in Python and cloud-native architectures, with a strong background in collaborating with scientists and mentoring junior researchers.

Highest-signal resume keywords

PhD In Machine LearningProficiency In PythonExperience With Reinforcement LearningDeep Experience With ML FrameworksHands-On Experience Training AI Models

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningMachine LearningPost-Training MethodsMolecular Representation LearningGenerative ChemistryAgentic AI SystemsEnd-To-End System DesignCloud EnvironmentsAI Model TrainingReward Modeling

Soft Skills

CollaborationMentoringResearch Communication

Tools & Technologies

PyTorchTensorFlowJAXHuggingFaceMLflowAWSAzureNextflowArgoKubernetes

Industry Keywords

BioinformaticsCheminformaticsMolecule DiscoveryScientific DiscoveryNeuroscienceChemistryAI ResearchML/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 domain tools, including RDKit, molecular graph ML, ELN/LIMS APIs, and instrument drivers
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 research, 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 autonomous scientific discovery and reduce DMTA turnaround
Transition AI prototypes into production-deployed systems

Requirements

What you’ll need

PhD in Machine Learning, Bioinformatics, Cheminformatics, Computer Science, or related discipline, or MS plus 3 years / BS plus 5 years equivalent experience
Demonstrated wet-lab collaboration or hands-on experience
Approximately 1–2 years of experience applying AI/ML in biology, chemistry, neuroscience, or a related scientific 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 cloud-native AWS/Azure pipeline architectures, including Nextflow and Argo on Kubernetes
Demonstrable research experience through project contributions and ideally publications in relevant ML/NLP venues
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 benefits
Dental benefits
Vision benefits
Prescription drug benefits
Flexible benefits, including healthcare and/or dependent day care flexible spending accounts
Life insurance and death benefits
Certain time off and leave of absence benefits
Well-being benefits, including employee assistance program, fitness benefits, and employee clubs and activities
Employee resource groups (ERGs) offering strong support networks