Senior MLOps Engineer - Hudl Focus at Hudl

, , Spain -

Full Time

Start Date

Immediate

Expiry Date

28 Apr, 26

Salary

111000.0

Posted On

28 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

MLOps, Python, CI/CD, Containerization, Linux Systems, Systems Thinking, Automation, Reliability, Edge Infrastructure, Data Science, Embedded Engineering, Video Technologies, Fleet Management, NVIDIA Edge Ecosystem, GStreamer, ffmpeg

Industry

Software Development

Description

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? Your Role 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. 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. 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. Must-Haves 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. Nice-to-Haves Edge AI Stack. Experience with the NVIDIA edge ecosystem (Jetson Nano/NX/Orin, DeepStream SDK, TensorRT) is a huge plus. Video Technologies. Familiarity with video pipelines, GStreamer, or ffmpeg. Fleet Management. Experience with tools like AWS IoT Greengrass, Balena, or custom OTA / fleet management solutions. Sports Passion. You have an interest in sports technology, video analytics, or performance metrics—but if not, we’ll teach you the domain. Our Role Champion work-life harmony. We’ll give you the flexibility you need in your work life (e.g., flexible vacation time above any required statutory leave, company-wide holidays and timeout (meeting-free) days, remote work options and more) so you can enjoy your personal life too. Guarantee autonomy. We have an open, honest culture and we trust our people from day one. Your team will support you, but you’ll own your work and have the agency to try new ideas. Encourage career growth. We’re lifelong learners who encourage professional development. We’ll give you tons of resources and opportunities to keep growing. Provide an environment to help you succeed. We've invested in our offices, designing incredible spaces with our employees in mind. But whether you’re at the office or working remotely, we’ll provide you the tech you need to do your best work. Support your wellbeing. Depending on location, we offer medical and retirement benefits for employees—but no matter where you’re located, we have resources like our Employee Assistance Program and employee resource groups to support your mental health. The base salary range for this role is displayed below—starting salaries will typically fall near the middle of this range. We make compensation decisions based on an individual's experience, skills and education in line with our internal pay equity practices. This role will also be eligible for a long-term incentive (LTI) award. Any bonuses awarded are based on individual and company performance paid at Hudl's discretion. Base Salary Range £66,000—£111,000 GBP Inclusion at Hudl Hudl is an equal opportunity employer. Through our actions, behaviors and attitude, we’ll create an environment where everyone, no matter their differences, feels like they belong. We offer resources to ensure our employees feel safe bringing their authentic selves to work, including employee resource groups and communities. But we recognize there’s ongoing work to be done, which is why we track our efforts and commitments in annual inclusion reports. We also know imposter syndrome is real and the confidence gap can get in the way of meeting spectacular candidates. Please don’t hesitate to apply—we’d love to hear from you. 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Responsibilities

As a Senior MLOps Engineer, you will build and scale the machine learning infrastructure for smart cameras and own the edge deployment pipelines. You will collaborate with cross-functional teams to ensure smooth integration of features and tackle complex physical challenges.