MLOps Engineer

At Hudl, we build great teams. We hire the best of the best to ensure you’re working with people you can constantly learn from. You’re trusted to get your work done your way while testing the limits of what’s possible and what’s next. We work hard to provide a culture where everyone feels supported, and our employees feel it—their votes helped us become one of the Top 100 Inspiring Workplaces.

We think of ourselves as the team behind the team, supporting the lifelong impact sports can have: the lessons in teamwork and dedication; the influence of inspiring coaches; and the opportunities to reach new heights. That’s why we help teams from all over the world see their game differently. Our products make it easier for coaches and athletes at any level to capture video, analyze data, share highlights and more.

Ready to join us?

Your Role

We're hiring a Senior MLOps Engineer for our Hardware Group to build and scale the machine learning infrastructure that powers Focus, our line of smart cameras. You'll own the edge deployment pipelines that transport neural networks from training clusters to tens of thousands of devices globally, and contribute to the platform that compiles trained models into optimised inference engines for devices like the Jetson Orin, building the "nervous system" for the next generation of automated sports capture.

As a Senior MLOps Engineer, you'll:

• Build scalable Edge infrastructure. You'll design, develop, and maintain the delivery systems that enable us to deploy models to fleets of devices.
• Own the model compilation platform. You'll build and maintain the pipeline that takes trained models and produces optimised, hardware-specific inference engines — managing TensorRT compilation, precision trade-offs (FP16/INT8), calibration, and engine validation to ensure models run reliably and efficiently on target devices.
• Work with cross-functional teams. You'll collaborate with Data Scientists, Embedded Engineers, and Product Managers to ensure smooth integration of complex features and capabilities
• Drive automation and reliability. You'll implement infrastructure to silently test candidate models on production devices and build telemetry pipelines to monitor drift, thermal impact, and inference latency in the wild.
• Solve complex physical challenges. You'll tackle the unique constraints of the edge - building resilient update mechanisms for low-bandwidth environments, optimising for limited storage, and ensuring devices recover gracefully from network failures.