Senior MLOps Engineer – Edge

This is a fully remote position, open to applicants in Spain, +1 more country.

📋 Description

• Create, develop, and sustain scalable edge delivery systems for the deployment of machine learning models across device fleets.

• Take ownership of the model compilation platform that transforms trained models into optimized, hardware-specific inference engines.

• Oversee TensorRT compilation, balancing FP16/INT8 precision, calibration, and engine validation.

• Work in collaboration with Data Scientists, Embedded Engineers, and Product Managers to integrate intricate features.

• Establish infrastructure for silent candidate-model testing on production devices.

• Construct telemetry pipelines to observe model drift, thermal effects, and inference latency.

• Develop resilient update mechanisms suitable for low-bandwidth environments and devices with limited storage.

• Ensure devices can recover smoothly from network disruptions.

• Set best practices in Python tooling, Infrastructure-as-Code, and CI/CD methodologies.

• Mentor and lead the team towards the development of robust, automated systems.

⛳️ Requirements

• Expertise in production MLOps for building and managing production model-deployment pipelines.

• Extensive experience with CI/CD processes, Docker, and Linux systems.

• Practical experience in compiling and optimizing machine learning models for embedded hardware.

• Understanding of precision, quantization, and validation of inference engines at scale.

• Capability to collaborate effectively with researchers and low-level embedded engineers.

• Skill in designing architectures that handle failures gracefully.

• Knowledge of deploying to a network of 10,000 heterogeneous devices.

• Familiarity with canary releases and safe rollback procedures.

• Proactive attitude with a willingness to address gaps and solve challenges.

• Experience with the NVIDIA edge ecosystem, including Jetson Orin, DeepStream SDK, and TensorRT is a plus.

• Familiarity with video pipelines, GStreamer, or ffmpeg is advantageous.

• Experience with AWS IoT Greengrass, Balena, or custom OTA/fleet-management solutions is beneficial.

• Interest in sports technology, video analytics, or performance metrics is a plus.

🏝️ Benefits

• Flexible vacation policy.

• Company-wide holidays.

• Timeout (meeting-free) days.

• Options for remote work.

• Access to professional development resources and opportunities.

• Tech stack and hardware provided for both office and remote work.

• Medical benefits available, depending on location.

• Retirement benefits provided, depending on location.

• Employee Assistance Program available.

• Employee resource groups supported.

• Resources for mental health support.

• Open and honest culture with a focus on autonomy.