Senior MLOps Engineer – Edge
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Job Description
Senior MLOps Engineer building edge AI infrastructure for Hudl’s smart sports cameras. Deploying optimized models across global device fleets and monitoring reliability.
Responsibilities:
- Design, develop, and maintain scalable edge delivery systems for deploying machine learning models to fleets of devices.
- Own the model compilation platform that converts trained models into optimized, hardware-specific inference engines.
- Manage TensorRT compilation, FP16/INT8 precision trade-offs, calibration, and engine validation.
- Collaborate with Data Scientists, Embedded Engineers, and Product Managers to integrate complex features.
- Implement infrastructure for silent candidate-model testing on production devices.
- Build telemetry pipelines to monitor model drift, thermal impact, and inference latency.
- Develop resilient update mechanisms for low-bandwidth environments and limited-storage devices.
- Ensure devices recover gracefully from network failures.
- Establish best practices in Python tooling, Infrastructure-as-Code, and CI/CD.
- Mentor and guide the team toward robust, automated systems.
Requirements:
- Production MLOps expertise building and operating production model-deployment pipelines
- Deep experience with CI/CD, Docker, and Linux systems
- Hands-on experience compiling and optimizing machine learning models for embedded hardware
- Understanding of precision, quantization, and inference-engine validation at scale
- Ability to collaborate with researchers and low-level embedded engineers
- Ability to design architectures that handle failures gracefully
- Understanding of deploying to 10,000 heterogeneous devices
- Knowledge of canary releases and safe rollbacks
- Initiative and willingness to fill gaps and solve problems
- NVIDIA edge ecosystem experience, Jetson Orin, DeepStream SDK, and TensorRT are advantageous
- Familiarity with video pipelines, GStreamer, or ffmpeg is advantageous
- Experience with AWS IoT Greengrass, Balena, or custom OTA/fleet-management solutions is advantageous
- Interest in sports technology, video analytics, or performance metrics is advantageous
Benefits:
- Flexible vacation time
- Company-wide holidays
- Timeout (meeting-free) days
- Remote work options
- Professional development resources and opportunities
- Tech stack and hardware for working in the office or remotely
- Medical benefits, depending on location
- Retirement benefits, depending on location
- Employee Assistance Program
- Employee resource groups
- Mental health support resources
- Open, honest culture and autonomy
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