WHO YOU’LL WORK WITH
This role sits at the intersection of firmware, controls, hardware, and product development, helping accelerate how powered athlete products are built. You’ll report into engineering leadership, partner closely with controls and firmware engineers, and influence cross-functional teams responsible for highly integrated mechatronic systems that bring innovative athlete experiences to life.

WHO WE ARE LOOKING FOR
Nike seeks a seasoned technical leader who thrives in ambiguity and is motivated by building systems that amplify the impact of an entire engineering organization. This person brings deep expertise in Artificial Intelligence, software engineering, simulation infrastructure, and learning-based systems, paired with the judgment and execution required to ship production-grade solutions. They are equally comfortable developing complex technical architectures, enabling engineering teams through tooling and automation, and translating emerging technologies into scalable business impact. The ideal candidate is a hands-on builder with strong curiosity, exceptional problem-solving skills, and experience accelerating development workflows in robotics, controls, embedded systems, or other complex hardware-software environments. They embrace modern Artificial Intelligence-assisted development practices and continuously look for opportunities to improve speed, quality, and innovation across the product lifecycle.

• Open to work in Beaverton, Oregon; in-person presence required.
• Master's degree with 10+ years of experience, or Doctor of Philosophy (PhD) with 7+ years of relevant experience.
• Master’s degree in Engineering, Computer Science, Artificial Intelligence, Robotics, or related field. Will accept any suitable combination of education, experience and training.
• Demonstrated expertise in production Artificial Intelligence and Machine Learning systems, simulation environments, infrastructure tooling, and embedded software development.
• Strong proficiency in Python, C++, modern Artificial Intelligence development platforms, agentic workflows, and learning-based control methodologies.

WHAT YOU’LL WORK ON
You’ll build the Artificial Intelligence-powered foundation that accelerates firmware and controls development for next-generation powered athlete products. By combining simulation, automation, tooling, and learning-based methods, you’ll help engineering teams iterate faster, reduce development friction, and bring innovative products to athletes more efficiently.

• Develop and deploy Artificial Intelligence-powered development workflows, coding assistants, and automation capabilities that improve the speed and quality of firmware and controls engineering.
• Build and scale simulation and sim-to-real environments that enable rapid testing, validation, and iteration before deployment on physical hardware.
• Create training, testing, and deployment frameworks that support reliable transitions from model development to real-time embedded implementation.
• Partner closely with firmware, controls, hardware, and product teams to establish tooling, infrastructure, and workflows that remove bottlenecks and accelerate execution.
• Design automated optimization, tuning, and evaluation systems that enhance controller performance while reducing manual engineering effort.
• Apply Machine Learning, pattern recognition, and data-driven techniques to identify regressions, uncover failure modes, and accelerate root-cause analysis.
• Support learning-based control initiatives through scalable data pipelines, orchestration systems, visualization tools, and production-quality engineering practices.

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.