Applied AI​/ML Engineer; Agents

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.

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.

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

• Build agents that perform experimental design — applying Bayesian optimization, active learning, or related sequential decision-making methods to decide what to compute or measure next, and to balance exploration and exploitation across long-running discovery campaigns

• Help close the loop between simulation and physical experiments so that outcomes become durable knowledge — feeding back into what agents know and how their models reason — compounding across campaigns.

• Develop strategies for multi-fidelity and multi-objective decision-making, where agents must trade off cost, time, and uncertainty across simulations and physical experiments

Interdisciplinary Collaboration

• Work closely with Chemists, Materials Scientists, and the rest of the Agent team to co-develop our core orchestration intelligence

• Work on customer projects and implement the direct needs required for these projects

• You are someone who gets excited about the opportunity to enable scientists to work on world changing challenges in this domain, with a personal interest in the potential applications of the technology that CuspAI is building.

• Proficiency in the modern ML ecosystem such as Py Torch or JAX, with experience taking ML-driven systems from prototype to production

• Strong software engineering skills (building systems at scale in a production environment): testing, modular design, CI/CD, and scalable ML operations in production environments

• If you have a PhD or Masters degree and 4-5 yrs industry experience this would be ideal, but we will also consider you if you have a PhD and slightly less experience in industry

• You have a proactive builder mentality with a bias toward shipping and iteration

• Willingness to learn about materials science, and in particular experimental chemistry - to learn the vocabulary of the discipline to meaningfully interact with other stakeholders

• Experienced in LLM-assisted programming, knowing its strengths and weaknesses thoroughly.

• Experience applying ML models specifically for materials science, chemistry, or drug discovery applications

• Experience with agentic frameworks and building LLM-powered applications

• Experience with sequential decision-making methods — Bayesian optimization, active learning, bandits, or reinforcement learning — applied to real-world systems

• Advanced agentic-reasoning techniques: planning models, self-improving systems, multi-tool agents, or RLHF/RLAIF workflows

This role could be based in our Cambridge, London, Amsterdam or Berlin offices, with the expectation of being in the office three days per week. Additionally, there may be regular travel required to our other offices for collaboration and project…