Applied AI/ML Engineer

About CuspAI

CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia. Our founding team is the most cited in the world, comprised of world-class researchers in AI, chemistry and engineering.

We are working on some of the hardest and most important challenges including energy, clean water, the future of compute, and carbon capture, and this is just the start of what our 'search engine' for next-generation materials will unlock.

We invite you to be part of a diverse, innovative team at the intersection of AI and materials science, working to create impactful partnerships that drive innovation, scalability, and industry collaboration. This work matters. Your work matters.

We’re on the cusp of the on-demand materials era. Join us.

The Role

Due to rapid growth in our core AI capabilities, we are seeking an experienced Applied AI/ML Engineer (Agents) to design and build the intelligent agents that power our autonomous materials discovery engine.

Your Impact

You will be instrumental in developing the "artificial brain" of our agentic materials discovery engine. This is responsible for orchestrating complex, closed-loop scientific workflows, autonomously making decisions, running simulations, and driving experimental campaigns to find breakthrough materials faster than ever before.

Your work will directly accelerate CuspAI’s path to finding solutions for global sustainability challenges.

What You Will Do

Agentic systems

• Design the agentic framework that powers our platform to discover new materials. This includes spanning dynamic, multi-stage simulation workflows from literature-grounded hypothesis generation through to computational and experimental validation

• Build the integration that connects agents to ML models, simulation engines, databases, and heterogeneous compute backends

• Design pipelines that let agents autonomously plan, schedule, execute, and interpret computational tasks at scale and over long periods of time

• Use expert annotations from the Chemistry team to drive targeted improvements in agent planning, retrieval, and decision-making

• Create evaluations to measure the effectiveness of agents

Experimental design