About the job
At Hudl, we cultivate exceptional teams. We seek out top talent to ensure you are surrounded by individuals from whom you can continually learn. You have the autonomy to accomplish your tasks in your own way, while pushing the boundaries of what is possible and what lies ahead. Our culture is designed to foster support and encouragement, reflected in our recognition as one of Newsweek's Top 100 Global Most Loved Workplaces.
We consider ourselves the backbone of the teams we support, recognizing the lifelong impact that sports can have through lessons in teamwork and dedication, the influence of inspiring coaches, and the opportunities to reach new heights. Our products empower teams worldwide to view their games from a different perspective, simplifying video capture, data analysis, highlight sharing, and more for coaches and athletes at any level.
Are you ready to be part of our journey?
Your Role
We are in search of a Senior MLOps Engineer to join our Hardware Group, where you will develop and scale the machine learning infrastructure that powers our innovative smart cameras, Focus. You will take ownership of the edge deployment pipelines that transport neural networks from training clusters to tens of thousands of devices worldwide. This role serves as a vital link between our Applied Machine Learning team in London and our Software squads in the Netherlands and the U. S., contributing to the 'nervous system' for the next generation of automated sports capture.
As a Senior MLOps Engineer, your responsibilities will include:
- Building scalable Edge infrastructure. You will design, develop, and maintain the systems that facilitate model deployment to fleets of devices, leading the re-architecture towards a dynamic, granular update system for enhanced learning speed.
- Collaborating with cross-functional teams. Partnering with Data Scientists, Embedded Engineers, and Product Managers to ensure seamless integration of complex features, transforming research requirements into deployable hardware solutions.
- Driving automation and reliability. You will implement infrastructure to silently test candidate models on production devices and build telemetry pipelines to monitor drift, thermal impact, and inference performance.
