Advanced Analytics Research Scientist - Industrial Edge Intelligence

Rockwell Automation is a global technology leader focused on helping the world’s manufacturers be more productive, sustainable, and agile. With more than 28,000 employees who make the world better every day, we know we have something special. Behind our customers - amazing companies that help feed the world, provide life-saving medicine on a global scale, and focus on clean water and green mobility - our people are energized problem solvers that take pride in how the work we do changes the world for the better.

We welcome all makers, forward thinkers, and problem solvers who are looking for a place to do their best work. And if that’s you we would love to have you join us!

Job Description We are looking for a inventive AI engineer/scientist to help build the next generation of AI-native industrial automation systems powered by latest edge compute platforms.

You will focus on developing intelligent systems capable of real-time learning, reasoning, optimization, and autonomous operation. These systems operate directly within industrial environments, bringing advanced AI capabilities closer to machines, processes, and operational decision making. You will work at the convergence of edge AI, industrial automation, autonomous systems, embedded intelligence, and real-time operational analytics to help define how you design and operate future industrial systems.

This role focuses on redefining industrial automation through AI-native system design. The company embeds intelligence directly at the edge, making it capable of learning, adapting, and operating autonomously in real-world environments. You will work on frontier challenges involving real-time AI, autonomous operation, optimization, and industrial-scale deployment while helping shape the future of intelligent industrial infrastructure.

You will report to the Director AI and have a hybrid work schedule, working from Austin, TX Responsibilities

Develop AI-native industrial systems capable of:

Real-time reasoning and decision making

Adaptive process optimization

Autonomous operational behaviors

Continuous learning from operational data Intelligent coordination across distributed industrial assets

Design and deploy AI solutions on latest edge compute platforms with advanced CPU/GPU acceleration capabilities

Build architectures that combine:

Generative AI and small language models

Machine learning and reinforcement learning

Optimization and control systems

Streaming analytics and event-driven intelligence

Distributed and edge-native AI inference

Ensure low-latency AI execution directly within industrial environments while balancing performance, reliability, and operational constraints

Integrate AI capabilities with industrial automation systems, robotics platforms, sensors, controllers, and operational infrastructure

Collaborate with diverse teams spanning AI research, controls engineering, embedded systems, and industrial operations

The Essentials

- You Will Have:

Bachelor's Degree in Relevant Field The Preferred
- You Might Also Have:

Typically requires 5+ years relevant experience

Background in the following:

Edge AI systems

Machine learning systems engineering

Autonomous systems

Industrial AI Embedded or distributed AI architectures

Experience developing and deploying AI applications in resource-constrained or real-time environments

Software engineering skills in Python and modern AI/ML frameworks

Familiarity with:

Edge inference optimization GPU-accelerated computing

Streaming and real-time systems

Distributed AI architectures

Control and optimization systems

Bridge advanced AI concepts with practical deployment in operational environments

Curiosity, creativity, and a passion for building intelligent systems that interact directly with the physical world

Bonus Experience…