Advisor - Agent Research
Eli Lilly and Company
August 12, 2026
Remote friendly (Indianapolis, IN)
United States
Clinical Research and Development
Responsibilities:
- Partner with scientists to build autonomous agents for molecule discovery tasks.
- Design and build reinforcement learning (RL) environments with appropriate state/action/termination semantics for discovery.
- 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) to execute real DMTA tasks.
- Build evaluation infrastructure (task suites, scoring harnesses, regression/experiment tracking e.g., MLflow).
Basic Qualifications:
- PhD (or MS + 3 yrs / BS + 5 yrs) in ML, Bioinformatics, Cheminformatics, Computer Science, or related field with demonstrated wet-lab collaboration/hands-on experience.
- ~1–2 years applying AI/ML in scientific disciplines (biology, chemistry, neuroscience, etc.).
- Hands-on experience training/post-training AI models.
Preferred Qualifications/Skills:
- Python; deep learning frameworks (PyTorch, TensorFlow, JAX, HuggingFace).
- RL and post-training methods (PPO, GRPO, DPO, reward modeling, RLHF/RLAIF) and libs (TRL, verl, or equivalents).
- Molecular representation learning/generative chemistry/protein-nucleic acid models.
- Agentic AI systems experience (OpenAI/Anthropic Agent SDK, LangChain, Smol agents).
- Cloud end-to-end system experience (APIs/frontends/agent platforms); GitHub portfolio a plus.
- Cloud-native pipeline knowledge (AWS/Azure), Nextflow/Argo on Kubernetes.
- Research contributions/publications; mentoring experience.
Benefits:
- Eligible for company bonus; 401(k), pension, vacation; medical/dental/vision/prescription; flexible benefits; life insurance; time off/leave; well-being benefits (EAP/fitness/clubs).
- Partner with scientists to build autonomous agents for molecule discovery tasks.
- Design and build reinforcement learning (RL) environments with appropriate state/action/termination semantics for discovery.
- 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) to execute real DMTA tasks.
- Build evaluation infrastructure (task suites, scoring harnesses, regression/experiment tracking e.g., MLflow).
Basic Qualifications:
- PhD (or MS + 3 yrs / BS + 5 yrs) in ML, Bioinformatics, Cheminformatics, Computer Science, or related field with demonstrated wet-lab collaboration/hands-on experience.
- ~1–2 years applying AI/ML in scientific disciplines (biology, chemistry, neuroscience, etc.).
- Hands-on experience training/post-training AI models.
Preferred Qualifications/Skills:
- Python; deep learning frameworks (PyTorch, TensorFlow, JAX, HuggingFace).
- RL and post-training methods (PPO, GRPO, DPO, reward modeling, RLHF/RLAIF) and libs (TRL, verl, or equivalents).
- Molecular representation learning/generative chemistry/protein-nucleic acid models.
- Agentic AI systems experience (OpenAI/Anthropic Agent SDK, LangChain, Smol agents).
- Cloud end-to-end system experience (APIs/frontends/agent platforms); GitHub portfolio a plus.
- Cloud-native pipeline knowledge (AWS/Azure), Nextflow/Argo on Kubernetes.
- Research contributions/publications; mentoring experience.
Benefits:
- Eligible for company bonus; 401(k), pension, vacation; medical/dental/vision/prescription; flexible benefits; life insurance; time off/leave; well-being benefits (EAP/fitness/clubs).