Advisor - Agent Research
Eli Lilly and Company
August 12, 2026
Remote friendly (San Francisco, CA)
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 for real discovery tasks (state/action/termination semantics).
- Curate and engineer reward functions from noisy scientific signals.
- Post-train domain models (SFT, DPO/GRPO/PPO, reward modeling, distillation) on chemistry/biology tasks.
- Integrate learned policies with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to execute real DMTA work.
- Build evaluation infrastructure (task suites, scoring harnesses, regression tracking, experiment tracking such as MLflow).
Basic Qualifications
- PhD (or MS + 3 yrs / BS + 5 yrs) in ML, Bioinformatics, Cheminformatics, Computer Science, or related field, with wet-lab collaboration/hands-on experience.
- ~1–2 years applying AI/ML to scientific disciplines; industry postdoc counts.
- Hands-on experience training or post-training AI models.
Additional Preferences
- Python; deep learning frameworks (PyTorch, TensorFlow, JAX, HuggingFace).
- RL/post-training methods (PPO, GRPO, DPO, reward modeling, RLHF/RLAIF) and libraries (e.g., TRL/verl or equivalents).
- Familiarity with molecular representation learning/generative chemistry/protein-nucleic acid models.
- Experience with agentic AI systems (OpenAI/Anthropic Agent SDK, LangChain, etc.).
- Cloud end-to-end systems experience; cloud-native pipelines (AWS/Azure, Nextflow, Argo on Kubernetes); research publications; mentoring experience.
Compensation & Benefits
- Anticipated wage: $151,500–$222,200; possible company bonus; comprehensive benefits (e.g., 401(k), medical/dental/vision, life insurance, time off, well-being benefits).
- Partner with scientists to build autonomous agents for molecule discovery tasks.
- Design and build reinforcement learning (RL) environments for real discovery tasks (state/action/termination semantics).
- Curate and engineer reward functions from noisy scientific signals.
- Post-train domain models (SFT, DPO/GRPO/PPO, reward modeling, distillation) on chemistry/biology tasks.
- Integrate learned policies with domain tools (RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to execute real DMTA work.
- Build evaluation infrastructure (task suites, scoring harnesses, regression tracking, experiment tracking such as MLflow).
Basic Qualifications
- PhD (or MS + 3 yrs / BS + 5 yrs) in ML, Bioinformatics, Cheminformatics, Computer Science, or related field, with wet-lab collaboration/hands-on experience.
- ~1–2 years applying AI/ML to scientific disciplines; industry postdoc counts.
- Hands-on experience training or post-training AI models.
Additional Preferences
- Python; deep learning frameworks (PyTorch, TensorFlow, JAX, HuggingFace).
- RL/post-training methods (PPO, GRPO, DPO, reward modeling, RLHF/RLAIF) and libraries (e.g., TRL/verl or equivalents).
- Familiarity with molecular representation learning/generative chemistry/protein-nucleic acid models.
- Experience with agentic AI systems (OpenAI/Anthropic Agent SDK, LangChain, etc.).
- Cloud end-to-end systems experience; cloud-native pipelines (AWS/Azure, Nextflow, Argo on Kubernetes); research publications; mentoring experience.
Compensation & Benefits
- Anticipated wage: $151,500–$222,200; possible company bonus; comprehensive benefits (e.g., 401(k), medical/dental/vision, life insurance, time off, well-being benefits).