Senior MLOps Engineer - Edge

About the role

The Senior MLOps Engineer will build and scale the machine learning infrastructure for Hudl's line of smart cameras. This role involves owning edge deployment pipelines to transport neural networks to devices globally and contributing to the platform that compiles models into optimized inference engines. You will work cross-functionally to ensure reliable model performance and automation across a fleet of devices.

Responsibilities

• Build and maintain scalable edge infrastructure for model delivery.
• Manage the model compilation platform, including TensorRT compilation and precision trade-offs.
• Collaborate with Data Scientists, Embedded Engineers, and Product Managers on feature integration.
• Implement infrastructure for automated model testing and telemetry monitoring for drift and latency.
• Develop resilient update mechanisms for low-bandwidth environments and limited storage constraints.
• Mentor team members on Python tooling, Infrastructure-as-Code, and CI/CD best practices.

Required skills

• MLOps
• CI/CD
• Docker
• Linux
• Edge inference
• TensorRT
• Python
• Infrastructure-as-Code

Nice to have

• NVIDIA Jetson Orin
• DeepStream SDK
• Video pipelines
• GStreamer
• ffmpeg
• AWS IoT Greengrass
• Balena

Benefits

• Flexible vacation time
• Company-wide holidays
• Meeting-free days
• Medical benefits
• Retirement benefits
• Employee Assistance Program

About the Company

Hudl is a sports technology company that helps teams from all over the world capture video, analyze data, and share highlights. They are recognized as one of Newsweek's Top 100 Global Most Loved Workplaces.