Advisor - Agent Research
Eli Lilly and Company
August 12, 2026
Remote friendly (Boston, MA)
United States
Other
Responsibilities
- Partner with scientists to build autonomous agents for molecule discovery.
- 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) for chemistry/biology tasks.
- Integrate learned policies with domain tools (e.g., RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to execute DMTA tasks.
- Build evaluation infrastructure: task suites, scoring harnesses, regression tracking, and 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 to scientific domains (biology, chemistry, neuroscience, etc.).
- Hands-on experience training or post-training AI models.
Additional Preferences (Preferred)
- Python; deep learning frameworks (PyTorch/TensorFlow/JAX/HuggingFace).
- RL and post-training methods (PPO/GRPO/DPO, reward modeling, RLHF/RLAIF) and libraries (e.g., TRL/verl).
- Experience with agentic AI systems; end-to-end cloud systems; AWS/Azure pipeline knowledge (Nextflow, Argo/Kubernetes).
- Publications/research contributions; mentoring experience.
Compensation/Benefits
- Anticipated wage: $151,500–$222,200.
- Eligible for company bonus and comprehensive benefits (e.g., 401(k), medical/dental/vision, flexible benefits, life insurance, time off, well-being benefits).
- Partner with scientists to build autonomous agents for molecule discovery.
- 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) for chemistry/biology tasks.
- Integrate learned policies with domain tools (e.g., RDKit, molecular graph ML, ELN/LIMS APIs, instrument drivers) to execute DMTA tasks.
- Build evaluation infrastructure: task suites, scoring harnesses, regression tracking, and 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 to scientific domains (biology, chemistry, neuroscience, etc.).
- Hands-on experience training or post-training AI models.
Additional Preferences (Preferred)
- Python; deep learning frameworks (PyTorch/TensorFlow/JAX/HuggingFace).
- RL and post-training methods (PPO/GRPO/DPO, reward modeling, RLHF/RLAIF) and libraries (e.g., TRL/verl).
- Experience with agentic AI systems; end-to-end cloud systems; AWS/Azure pipeline knowledge (Nextflow, Argo/Kubernetes).
- Publications/research contributions; mentoring experience.
Compensation/Benefits
- Anticipated wage: $151,500–$222,200.
- Eligible for company bonus and comprehensive benefits (e.g., 401(k), medical/dental/vision, flexible benefits, life insurance, time off, well-being benefits).