Advisor - Agent Research
Eli Lilly and Company
August 12, 2026
Remote friendly (San Diego, CA)
United States
Clinical Research and Development
Position Summary
- Design the learning layer of a scientific agent platform to enable agents to improve from experimental feedback; translate wet-lab and computational endpoints into trainable signals.
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.
- Curate and engineer reward functions from noisy scientific signals.
- 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 DMTA tasks.
- Build evaluation infrastructure: task suites, scoring harnesses, regression and experiment tracking (e.g., MLflow).
- Represent Frontier AI in the AI research community (publish, talks, reviews) and scout trends.
- Evaluate external vendors, open-source projects, and academic collaborations.
Basic Qualifications
- PhD (or MS + 3 yrs / BS + 5 yrs equivalent) in Machine Learning, Bioinformatics, Cheminformatics, Computer Science, or related field with demonstrated wet-lab collaboration/hands-on experience.
- ~1–2 years applying AI/ML in scientific disciplines (industry postdoc counts).
- Hands-on experience training/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 (TRL/verl or equivalent).
- Molecular representation learning, generative chemistry, or protein/nucleic acid models.
- Agentic AI systems (OpenAI/Anthropic Agent SDK, Langchain, Smol agents).
- Cloud end-to-end systems; AWS/Azure; Nextflow/Argo on Kubernetes.
- Publications in relevant ML/NLP venues; mentoring experience.
Application/Benefits
- None explicitly included beyond compensation/benefits: $151,500–$222,200 + potential company bonus; comprehensive benefits including 401(k), pension, vacation, medical/dental/vision/prescription, flexible benefits, life insurance, time off/leave, and well-being benefits.
- Design the learning layer of a scientific agent platform to enable agents to improve from experimental feedback; translate wet-lab and computational endpoints into trainable signals.
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.
- Curate and engineer reward functions from noisy scientific signals.
- 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 DMTA tasks.
- Build evaluation infrastructure: task suites, scoring harnesses, regression and experiment tracking (e.g., MLflow).
- Represent Frontier AI in the AI research community (publish, talks, reviews) and scout trends.
- Evaluate external vendors, open-source projects, and academic collaborations.
Basic Qualifications
- PhD (or MS + 3 yrs / BS + 5 yrs equivalent) in Machine Learning, Bioinformatics, Cheminformatics, Computer Science, or related field with demonstrated wet-lab collaboration/hands-on experience.
- ~1–2 years applying AI/ML in scientific disciplines (industry postdoc counts).
- Hands-on experience training/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 (TRL/verl or equivalent).
- Molecular representation learning, generative chemistry, or protein/nucleic acid models.
- Agentic AI systems (OpenAI/Anthropic Agent SDK, Langchain, Smol agents).
- Cloud end-to-end systems; AWS/Azure; Nextflow/Argo on Kubernetes.
- Publications in relevant ML/NLP venues; mentoring experience.
Application/Benefits
- None explicitly included beyond compensation/benefits: $151,500–$222,200 + potential company bonus; comprehensive benefits including 401(k), pension, vacation, medical/dental/vision/prescription, flexible benefits, life insurance, time off/leave, and well-being benefits.