Advisor - Agent Research

About the Role

We are rebuilding the Design-Make-Test-Analyze (DMTA) cycle, infusing scientific automation with foundation models, multi-agent systems, and robotics to make scientific discovery intelligent, autonomous, and fast. We're seeking a scientist-engineer hybrid to design the learning layer of our scientific agent platform. You will design the environments, rewards, and domain-specific models that enable agents to improve based on experimental feedback. You'll translate wet-lab and computational endpoints into a trainable signal to build models that plan and act against them.

Responsibilities

• Partner with scientists to build autonomous agents that undertake molecule discovery tasks
• Design and build reinforcement learning (RL) environments that wrap real discovery tasks with appropriate state, action, and termination semantics
• Curate and engineer reward functions from noisy scientific signal
• Post-train domain models (SFT, DPO/GRPO/PPO, reward modeling, distillation) on chemistry and biology tasks
• Integrate learned policies with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) so trained models execute real DMTA tasks
• Build the eval infrastructure: task suites, scoring harnesses, regression tracking, and experiment tracking (e.g., 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

What Success Looks Like

• Trained models that measurably outperform prompted frontier baseline models on internal discovery tasks
• Reward and evaluation infrastructure that other teams adopt as the default way to measure agent performance
• Measurable reduction in DMTA turnaround through autonomous planning and execution
• Seamless transition from prototype to production-deployed AI systems

Basic Qualifications

• PhD (or MS + 3 yrs / BS + 5 yrs equivalent experience) in Machine Learning, Bioinformatics, Cheminformatics, Computer Science, or related STEM field with demonstrated wet-lab collaboration or hands-on experience
• Approximately 1-2 years of demonstrated experience in applying AI/ML in scientific disciplines such as biology, chemistry, neuroscience, or a related field (industry postdoc counts)
• Hands-on experience training or post-training AI models

Additional Preferences

• Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch, Tensorflow, JAX, HuggingFace)
• Experience with RL and post-training methods (PPO, GRPO, DPO, reward modeling, RLHF/RLAIF) and libraries such as TRL, verl, or equivalent in-house stacks
• Familiarity with molecular representation learning, generative chemistry, or protein/nucleic acid models
• Hands-on experience building agentic AI systems (e.g., OpenAI/Anthropic Agent SDK, Langchain, Smol agents)
• Experience designing and shipping end-to-end systems in cloud environments (backend APIs, lightweight frontends, and agentic platforms) - GitHub portfolio a plus
• Working knowledge of cloud-native (AWS/Azure) pipeline architectures, including Nextflow, Argo on Kubernetes
• Demonstrable research experience, evidenced by contributions to projects, and ideally through publications in relevant ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)
• Experience mentoring and guiding junior researchers or engineers

Pay & Benefits

• Anticipated wage: $151,500 - $222,200
• Full-time equivalent employees eligible for a company bonus (depending, in part, on company and individual performance)
• Comprehensive benefit program including: 401(k); pension; vacation benefits; medical, dental, vision and prescription drug benefits; flexible 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 (employee assistance program, fitness benefits, and employee clubs and activities)