Applied AI/ML Engineer (Agents)

Design and build the intelligent agents that power CuspAI's autonomous materials discovery engine. You will be instrumental in developing the "artificial brain" 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.

What you'll do

• Design the agentic framework that powers the platform to discover new materials, 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
• 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
• Help close the loop between simulation and physical experiments so that outcomes become durable knowledge feeding back into what agents know
• Work closely with Chemists, Materials Scientists, and the rest of the Agent team to co-develop core orchestration intelligence
• Work on customer projects and implement the direct needs required for these projects

What you'll need

• Proficiency in the modern ML ecosystem such as PyTorch 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
• PhD or Masters degree and 4-5 years industry experience, or PhD with slightly less experience in industry
• Proactive builder mentality with a bias toward shipping and iteration
• Willingness to learn about materials science, and in particular experimental chemistry
• Experienced in LLM-assisted programming, knowing its strengths and weaknesses thoroughly

Nice to have

• 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

What they offer

• Competitive salary and equity in CuspAI
• 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays
• Gold Standard parental leave: 26 weeks (primary caregiver) and 12 weeks (secondary caregiver) at full pay
• Professional development budget to stay up to date with the latest industry knowledge