Advisor - Agent Research

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.

• 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.

• 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.

• Python; deep learning frameworks (PyTorch/Tensor Flow/JAX/Hugging Face).
• 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.

• 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.