AI/ML Scientist – Reinforcement Learning, Simulation & Optimization

About the role

Sustainably pioneer breakthroughs in healthcare. Join Siemens Healthineers’ Digital Technology & Innovation (DTI) organization as an AI Scientist – Reinforcement Learning & Operational Twinning. Develop next-generation AI systems that optimize healthcare operations through intelligent simulation, sequential decision-making, and digital twin technologies.

Responsibilities

• Design and develop reinforcement learning, simulation, and optimization algorithms for Operational Twinning applications in healthcare.
• Build intelligent decision-making systems that optimize scheduling, patient flow, triage, staffing, and resource utilization across complex healthcare environments.
• Develop AI-driven simulation environments and workflow models capable of representing real-world clinical and operational systems.
• Conduct original research in reinforcement learning, sequential decision-making, combinatorial optimization, neural optimization, and hybrid AI–operations research (AI-OR) methods.
• Translate large-scale operational and healthcare datasets into actionable optimization and policy-learning solutions.
• Rapidly prototype, evaluate, and validate novel algorithmic approaches for feasibility, scalability, explainability, and operational impact.
• Collaborate with multidisciplinary R&D teams to integrate advanced optimization and simulation technologies into Siemens Healthineers’ digital health platforms.
• Publish scientific research, contribute to patents, and drive innovation in operational AI and healthcare optimization technologies.
• Stay current with advancements in reinforcement learning, world models, AI simulation, operations research, and autonomous decision systems.

Requirements

• Ph.D. in Computer Science, Applied Mathematics, Operations Research, Electrical Engineering, Robotics, Artificial Intelligence, or a related technical field.
• Strong hands-on experience in reinforcement learning, sequential decision-making systems, or simulation-based optimization.
• Experience developing optimization algorithms, operational AI systems, or digital twin/simulation environments.
• Strong programming skills in Python and modern ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience translating complex real-world operational problems into scalable AI-driven solutions.
• Strong technical communication skills and demonstrated research contributions through publications, patents, or applied research projects.

Preferred Qualifications

• Experience with digital twins, workflow simulation, world models, or autonomous systems.
• Background in operations research, combinatorial optimization, stochastic systems, or control theory.
• Familiarity with healthcare operations, hospital systems, or clinical workflow optimization.
• Experience deploying AI solutions into production or cloud-based environments (AWS, Azure, GCP).
• Industry or applied research experience developing operational AI systems at scale.