Applied AI/ML Engineer (Agents)

About the role

CuspAI is building an autonomous materials discovery engine and needs an Applied AI/ML Engineer to create the intelligent agents that drive the system. You will design the "artificial brain" that orchestrates closed‑loop scientific workflows, makes autonomous decisions, runs simulations, and guides experimental campaigns. This role directly accelerates the company’s mission to solve global sustainability challenges through AI‑enabled materials breakthroughs.

Key responsibilities

• Design and implement the agentic framework that spans hypothesis generation, multi‑stage simulations, and experimental validation.
• Build integrations linking agents to ML models, simulation engines, databases, and heterogeneous compute back‑ends.
• Develop pipelines enabling agents to autonomously plan, schedule, execute, and interpret large‑scale computational tasks.
• Create evaluation metrics to measure agent effectiveness and iterate on performance.
• Implement experimental‑design agents using Bayesian optimization, active learning, or related sequential decision‑making methods.
• Close the loop between simulation results and physical experiments, feeding knowledge back into agent reasoning.
• Collaborate closely with chemists, materials scientists, and the broader Agent team to co‑develop core orchestration intelligence.
• Support customer projects by tailoring agent capabilities to specific needs.

Required profile

• Strong enthusiasm for enabling scientists to tackle world‑changing challenges.
• Proficiency with modern ML ecosystems such as PyTorch or JAX and experience moving ML‑driven systems from prototype to production.
• Robust software‑engineering background: testing, modular design, CI/CD, and scalable ML operations.
• PhD or Master’s degree with 4–5 years of industry experience (or equivalent).
• Proactive builder mentality with a bias toward shipping and iteration.
• Willingness to learn materials‑science terminology and collaborate with experimental chemists.
• Hands‑on experience with LLM‑assisted programming.

Required skills

• Python
• PyTorch
• JAX
• CI/CD pipelines
• Software testing and modular design
• Scalable ML operations
• LLM‑assisted programming
• Bayesian optimization
• Active learning
• Reinforcement learning

What we offer

• Competitive salary and equity participation.
• Generous holiday allowance (28 days in NL) and premium parental leave.
• Professional development budget for continuous learning.
• Opportunity to work on high‑impact problems in AI‑driven materials science.
• Collaborative interdisciplinary environment bridging AI research, computational chemistry, and experimental science.