Applied AI/ML Engineer (Agents)
Core
Design and build intelligent agents that orchestrate closed-loop scientific workflows for autonomous materials discovery, connecting ML models, simulations, and experiments.
Role type
Senior Applied AI/ML Engineer (Agents)
Builds
Agentic framework for multi-stage simulation workflows and integration with ML models, simulation engines, and heterogeneous compute backends
Domain
AI and materials science (chemistry, energy, carbon capture)
Deliverable
production ML models
Required skills
PyTorch or JAX, software engineering for production systems, CI/CD, scalable ML operations, LLM-assisted programming, Bayesian optimization, active learning, sequential decision-making
Preferred skills
agentic frameworks, LLM-powered applications, multi-fidelity decision-making, RLHF/RLAIF workflows
Technologies
PyTorch, JAX, LLMs, simulation engines, databases
Responsibilities
Design agentic framework for dynamic simulation workflows; Build integration connecting agents to ML models and compute backends; Create evaluations to measure agent effectiveness; Build agents for experimental design using Bayesian optimization; Work with Chemists and Materials Scientists to co-develop orchestration intelligence
Seniority
Senior, hands-on IC