Fast Facts
Hudl is looking for a Senior MLOps Engineer to build and scale machine learning infrastructure for their smart cameras and manage deployment pipelines globally. This role requires collaboration across teams and aims to optimize deployment for edge environments.
Responsibilities: Key responsibilities include designing scalable edge infrastructure, collaborating with cross-functional teams, implementing automation and reliability solutions, solving complex physical challenges, and mentoring the team in MLOps best practices.
Skills: Experience in production MLOps focusing on IoT and Edge, CI/CD processes, containerization with Docker, and Linux systems are essential, along with a collaborative mindset for hardware deployment.
Qualifications: Preferred qualifications include familiarity with NVIDIA edge technologies, video processing tools, fleet management solutions, and a passion for sports technology.
Location: London or Remote (U.K.)
Compensation: Not provided by employer. Typical compensation ranges for this position are between £80,000 - £120,000.
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 office in London, but we're also open to remote candidates in the UK.
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; thiis 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.
Compensation
