Senior MLOps Engineer – Edge AI Deployments

London, United Kingdom;
United Kingdom (Remote)

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 Newsweek's Top 100 Global Most Loved 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?

Playing in picture-in-picture

Playing in picture-in-picture (opens a new window)

00:00

01:07

Quality Auto

Speed Normal

CC/subtitles Off

We’re hiring a Senior MLOps Engineer to join our Hardware Group, where you’ll build and scale the machine learning infrastructure that powers our smart cameras, Focus. You’ll own the edge deployment pipelines that transport neural networks from training clusters to tens of thousands of devices globally and will act as the bridge between our Applied Machine Learning team in London and our Software squads in the Netherlands and the U.S., building the "nervous system" for the next generation of automated sports capture.

• Build scalable Edge infrastructure. You’ll design, develop, and maintain the delivery systems that enable us to deploy models to fleets of devices. You will lead the re-architecture to a dynamic, granular update system allowing faster learning.
• 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, translating research requirements into deployable hardware realities.
• 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.
• Mentor and lead. You’ll share your MLOps expertise to establish best practices in Python tooling, Infrastructure-as-Code, and CI/CD, guiding the team toward a more robust, automated future.

We'd like to hire someone for this role who lives near our offices in London or Barcelona, but we're also open to remote candidates in the UK and Spain.

• Experienced in production MLOps. You’ve played a key role in building and operating pipelines that deploy models to production—specifically dealing with the "physical world" (IoT, Edge, Robotics) rather than just cloud APIs.
• Technical expertise. You write clean, maintainable infrastructure code and have deep experience with CI/CD pipelines, containerization (Docker), and Linux systems.
• Collaborative. You understand that shipping to hardware is a team sport and can communicate effectively with researchers and low-level embedded engineers to translate constraints into solutions.
• Systems Thinking. You can design architectures that handle failure gracefully and understand the implications of deploying to 10,000 heterogeneous devices, including how to manage risk via canary releases and safe rollbacks.
• Bias towards action: You see your role as solving problems; this means filling gaps and taking initiative as needed to help the team win together.

• Edge AI Stack.
  
  Experience with the NVIDIA edge ecosystem (Jetson Nano/NX/Orin, Deep Stream SDK, Tensor
  
  RT) is a huge plus.
• Video Technologies. Familiarity with video pipelines, GStreamer, or ffmpeg.
• Fleet Management. Experience with tools like AWS IoT Green grass, Balena, or custom OTA / fleet…