1 day ago

Applied AI/ML Engineer (Agents/RL)

CuspAI

Amsterdam

1 day ago

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CuspAI seeks an experienced Applied AI/ML Engineer to design intelligent agents for autonomous materials discovery. The role involves building agentic frameworks, integrating ML models, and applying experimental design methods. Requires proficiency in PyTorch or JAX, strong software engineering skills, and a passion for enabling scientific breakthroughs.

AIHybridFull-timeSeniorPyTorchJAX

Applied AI/ML Engineer (Agents/RL)

Apply

AIHybridFull-timeSeniorPyTorch

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

Hybrid: 3 days per week in office

Experience Required

5 years

Employment Type

Full-time

Experience Level

PhD or Masters with 4-5 years industry experience

Core Qualifications

Technical (Must-have)

PyTorchJAXML-driven systemsEngineeringTestingModular DesignCI/CDScalable ML OperationsLLM-assisted Programming

Soft Skills

Proactive Builder MentalityBias toward shipping and iterationWillingness to learnCollaboration

Preferred Qualifications

Technical (Nice-to-have)

Bayesian optimizationActive LearningBanditsReinforcement LearningAgentic FrameworksLLM-powered ApplicationsSequential Decision-makingMulti-tool AgentsRLHFRLAIF

Key Responsibilities

•Design the agentic framework for autonomous materials discovery spanning simulation workflows from hypothesis generation to validation
•Build integration connecting agents to ML models, simulation engines, databases, and heterogeneous compute backends
•Design pipelines for agents to autonomously plan, schedule, execute, and interpret computational tasks at scale
•Use expert annotations to drive improvements in agent planning, retrieval, and decision-making
•Create evaluations to measure agent effectiveness
•Build agents that perform experimental design using Bayesian optimization, active learning, or sequential decision-making
•Help close the loop between simulation and physical experiments to compound knowledge across campaigns
•Develop strategies for multi-fidelity and multi-objective decision-making balancing cost, time, and uncertainty
•Work closely with Chemists, Materials Scientists, and the Agent team to co-develop orchestration intelligence
•Work on customer projects and implement direct needs

AIMLAgentsReinforcement LearningMaterials SciencePyTorchJAXFull-timeHybridSenior

Key Responsibilities

•Design the agentic framework for autonomous materials discovery spanning simulation workflows from hypothesis generation to validation

•Build integration connecting agents to ML models, simulation engines, databases, and heterogeneous compute backends

•Design pipelines for agents to autonomously plan, schedule, execute, and interpret computational tasks at scale

•Use expert annotations to drive improvements in agent planning, retrieval, and decision-making

•Create evaluations to measure agent effectiveness

•Build agents that perform experimental design using Bayesian optimization, active learning, or sequential decision-making

•Help close the loop between simulation and physical experiments to compound knowledge across campaigns

•Develop strategies for multi-fidelity and multi-objective decision-making balancing cost, time, and uncertainty

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

•Work on customer projects and implement direct needs