Job description

WHO YOU’LL WORK WITH This role sits at the center of a multidisciplinary team developing the next generation of powered athlete products. The Principal Controls Engineer will partner closely with controls, firmware, hardware, sports science, and product teams to bring intelligent mechatronic systems to life. This individual will provide technical leadership, influence product direction, and collaborate across research and engineering functions to deliver breakthrough athlete experiences. WHO WE ARE LOOKING FOR Nike is seeking a seasoned controls expert who thrives in ambiguity and is energized by transforming advanced concepts into commercial-grade products. This individual brings deep experience in real-time control systems, embedded software, machine intelligence, and human-in-the-loop product development. They have a strong track record of shipping complex electromechanical products and possess the judgment, technical leadership, and systems thinking required to solve challenging problems at the intersection of hardware, software, and human performance. The ideal candidate combines expertise in modern control methodologies with a passion for innovation. They are equally comfortable developing algorithms, guiding technical architecture, building development tools, and collaborating with cross-functional teams. They embrace emerging technologies, including artificial intelligence-assisted development, and continuously look for opportunities to accelerate learning, iteration, and impact. Most importantly, they are motivated by creating products that feel seamless, intuitive, and transformative for athletes. Open to work in Beaverton, Oregon (in-person required) 10 years of experience with a Master’s degree, or 7 years of experience with a PhD, in controls engineering, robotics, electrical engineering, mechanical engineering, computer science, or a related field Master’s degree in Engineering, Computer Science, Robotics, Controls Engineering, or related field. Will accept any suitable combination of education, experience and training Demonstrated expertise in Model Predictive Control (MPC), Linear Quadratic Regulator (LQR), Kalman filtering, sensor fusion, embedded controls, and machine learning systems Experience deploying real-time algorithms in C/C , leading technical programs, and building commercial electromechanical products WHAT YOU’LL WORK ON You will define how powered athlete products sense, decide, and act in real time. Working from early concepts through commercialization, you will develop advanced control systems, build intelligent embedded solutions, and collaborate with world-class partners to create responsive, safe, and athlete-centered experiences. Develop and deploy real-time control algorithms that determine how and when assistance is delivered on resource-constrained embedded hardware Apply advanced control techniques including Model Predictive Control (MPC), Linear Quadratic Regulator (LQR), Kalman filtering, sensor fusion, data-driven control, and learning-based control to complex product challenges Design systems that predict athlete intent and gait from noisy sensor inputs and translate those insights into precise, responsive motor behavior Partner closely with firmware, hardware, product, and sports science teams to validate solutions through laboratory, simulation, and on-body testing Build custom simulation environments, data analysis pipelines, automation tools, and development workflows that accelerate controller development and product maturity Advance embedded machine learning capabilities and edge-based intelligence to improve product adaptability and performance Establish performance metrics, monitor system behavior, identify opportunities for improvement, and drive enhancements across the product lifecycle We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form .

Requirements

Must-haves

Open to work in Beaverton, Oregon (in-person required)
10 years of experience with a Master’s degree, or 7 years of experience with a PhD, in controls engineering, robotics, electrical engineering, mechanical engineering, computer science, or a related field
Master’s degree in Engineering, Computer Science, Robotics, Controls Engineering, or related field. Will accept any suitable combination of education, experience and training
Demonstrated expertise in Model Predictive Control (MPC), Linear Quadratic Regulator (LQR), Kalman filtering, sensor fusion, embedded controls, and machine learning systems
Experience deploying real-time algorithms in C/C , leading technical programs, and building commercial electromechanical products
Develop and deploy real-time control algorithms that determine how and when assistance is delivered on resource-constrained embedded hardware
Apply advanced control techniques including Model Predictive Control (MPC), Linear Quadratic Regulator (LQR), Kalman filtering, sensor fusion, data-driven control, and learning-based control to complex product challenges
Design systems that predict athlete intent and gait from noisy sensor inputs and translate those insights into precise, responsive motor behavior
Partner closely with firmware, hardware, product, and sports science teams to validate solutions through laboratory, simulation, and on-body testing
Build custom simulation environments, data analysis pipelines, automation tools, and development workflows that accelerate controller development and product maturity
Advance embedded machine learning capabilities and edge-based intelligence to improve product adaptability and performance
Establish performance metrics, monitor system behavior, identify opportunities for improvement, and drive enhancements across the product lifecycle

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningdesign systems

Platform: workday
Effort: long