Michael Lindstedt

About Michael Lindstedt

Mike is an innovative engineer who graduated from the prestigious Siemens Mobility Development Program, where he distinguished himself through exceptional contributions across multiple engineering disciplines. He now serves as a Optimization and AI System Engineer, where he applies his passion for innovation at the intersection of advanced mathematics, software engineering, and business strategy.In his new role, Mike will be training with research scientists and engineers specialized in mathematical optimization and operations research. He\'ll contribute to cutting-edge research, engineering, and/or academic publications while developing expertise to enhance Siemens\' local expertise in the Rail Network Optimization Portfolio. He also leads AI initiatives that strengthen Siemens\' unique selling propositions in the railway industry.Mike\'s research passion was ignited through his corporate work developing an adaptive Kalman filter algorithm that significantly enhanced the performance of an established technology. This innovation resulted in a patent application and demonstrated his ability to apply theoretical concepts to solve real-world challenges. His experience spans from rigorous algorithm development to practical implementation, showcasing his versatility as both a researcher and applied engineer.His expertise encompasses machine learning, control theory, mathematical optimization, operations research, and artificial intelligence—particularly applying advanced mathematical concepts to practical problems. Mike is also deeply interested in business strategy and leveraging emerging technologies like generative AI to drive innovation within engineering workflows.As he advances in his career, Mike aims to become a leader in advanced mathematical techniques such as optimization and AI, bridging cutting-edge research with practical applications that create transformative value. He consistently seeks opportunities where disruptive thinking and technical excellence can converge to develop solutions with significant real-world impact.All views expressed are solely his and do not reflect those of his employer.

Experience

1. Optimization and Ai System Engineer

   Siemens

   Jul 2025 — Present · Pittsburgh, PA, US

   Formulated a novel algorithmic framework combining Graph Neural Networks (GNNs), Imitation Learning (IL) and Reinforcement Learning (RL) to learn an optimal node-selection strategy in Branch and Bound tree searches, deriving model architecture and mathematical functions to optimize for solution quality within strict time constraints.• Designed a self-optimizing software architecture for an agentic LLM database entry generation system that utilizes feedback loops to refine both data acquisition and synthesis.• Conceptualized a dual-stage RL framework where tool-call strategies are optimized for gathering relevant data and final database entries generated from relevant data are refined via RLHF, leveraging user quality rating data as a signal to adapt the system\'s strategies to specific user-base preferences.• Engineered a modular, parallelized data preprocessing pipeline that enables scalable ingestion of diverse problem instances, significantly reducing training data preparation time through automation and efficient parallelization.• Training with research scientists and engineers to learn mathematical optimization, operations research, and our suite of software solutions built on these technologies.• Bringing advanced optimization technologies and expertise to the US market.• Coordinating a cross-functional AI strategy initiative to set business objectives for AI-enabled process improvement and new product features.• Driving execution of our AI Strategy initiative through coordinating implementation steps with key stakeholders.

Education

• Grove City College

  Minor, Robotics

• Sharpsville Area Senior High School

  Class Valedictorian

• Grove City College

  Bachelor of Science - BS, Electrical Engineering with Computer Engineering Concentration