Advisor, Agent Discovery & RL for Molecule Design

Scorpion Therapeutics; San Diego (CA); On-site; USD 152,000 - 222,000; Full time

Benefits offered by this job

401(k) and pension

Medical/dental/vision

Paid time off

Well-being benefits

Life insurance

Job summary

Scorpion Therapeutics seeks a PhD-level ML scientist to design the learning layer of a scientific agent platform, translating wet-lab and computational endpoints into trainable signals. You will enable agents to improve from experimental feedback and collaborate with scientists on molecule discovery tasks.

You will design RL environments, engineer reward functions, and post-train domain models on chemistry and biology tasks, integrating policies with RDKit and ELN/LIMS APIs, and building

Qualifications

• PhD or MS/BS with wet-lab collaboration experience.
• 1–2 years applying AI/ML in scientific disciplines.
• Hands-on experience training/post-training AI models.

Responsibilities

• Design the learning layer of a scientific agent platform for improved experiments.
• Partner with scientists to build autonomous agents for molecule discovery tasks.
• Design and build RL environments with proper state/action semantics.
• Curate reward functions from noisy scientific signals.
• Post-train domain models on chemistry and biology tasks.
• Integrate learned policies with RDKit, ML tools, and LIMS APIs to execute DMTA tasks.
• Build evaluation infra: task suites, scoring, and experiment tracking (MLflow).
• Represent Frontier AI in the research community through publications and talks.

Skills

Python

PyTorch

RL (PPO, DPO)

Molecular representation learning

Cloud & Kubernetes

Education

PhD in ML / Bioinformatics / Cheminformatics

MS + 3 yrs / BS + 5 yrs equivalent

Tools

RDKit

ELN/LIMS APIs

MLflow

HuggingFace

Job description

Scorpion Therapeutics seeks a PhD-level ML scientist to design the learning layer of a scientific agent platform, translating wet-lab and computational endpoints into trainable signals. You will enable agents to improve from experimental feedback and collaborate with scientists on molecule discovery tasks.

You will design RL environments, engineer reward functions, and post-train domain models on chemistry and biology tasks, integrating policies with RDKit and ELN/LIMS APIs, and building