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Ultralytics
Ultralytics
11 – 50 Employees
ConsultingLogisticsManufacturing
Ultralytics builds AI tools for creating, training, and deploying machine learning models. Its Ultralytics HUB platform works with the Ultralytics YOLO computer vision framework, helping teams develop and manage vision applications across fields such as manufacturing, logistics, agriculture, and healthcare. The company’s work centers on making computer vision model development accessible to a range of users and practical industry needs.
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Embedded Computer Vision Engineer at Ultralytics (On-site, Spain)

Bring Ultralytics YOLO models from PyTorch into production on edge devices. Build runtime integrations and tune deployment for CPU, GPU, NPU, and SoC platforms.

ESSpainOn-siteFull-timeMiddleSeniorEnglish RequiredComputer Vision EngineerMachine Learning Engineer
C++DockerLinuxPythonPyTorch

Description

  • Develop and maintain model export and inference integrations, from conversion through on-device support.
  • Keep integrations compatible with Python, PyTorch, and changing vendor SDKs.
  • Review partner code and verify integrations on physical hardware before release.
  • Prepare embedded boards and development kits for YOLO deployment and testing.
  • Tune models for target devices, balancing latency, memory use, and accuracy.
  • Investigate accuracy drops introduced by conversion or INT8 quantization.
  • Measure and profile YOLO tasks and model generations, then document the results.
  • Manage technical partnerships for hardware and silicon platforms, including AMD, Google, and Nvidia integrations.
  • Run engineering syncs with partners and set integration guidelines.
  • Turn platform limitations into model, runtime, and deployment requirements.
  • Coordinate with partner compiler teams to support new YOLO architectures at launch.
  • Troubleshoot issues involving models, runtimes, drivers, and devices.
  • Maintain hardware CI across self-hosted and partner-hosted devices, along with deployment Docker images.
  • Produce integration documentation and device benchmark pages.
  • Create developer resources, host live sessions, and demonstrate the work at industry events.
  • Help developers and customers through GitHub, the community forum, and the sales team.

Requirements

  • Strong grounding in machine learning or computer vision, including an understanding of YOLO models.
  • Strong Python skills and practical experience with PyTorch and ONNX.
  • Hands-on experience with TensorRT, OpenVINO, ONNX Runtime, or a vendor NPU SDK.
  • Experience with INT8 quantization and calibration, including diagnosing accuracy loss after conversion.
  • Practical experience deploying on edge devices such as NVIDIA Jetson, Raspberry Pi, or NPU development boards.
  • Experience flashing, configuring, and troubleshooting deployments on resource-constrained hardware.
  • Comfort working with Linux, Docker, and Python dependency management across platforms.
  • Sound software engineering habits, including clean code, Git pull requests, and code review.
  • Clear written English for documentation and collaboration.
  • Senior-level ability to own technical integrations end to end with minimal oversight.
  • Enthusiasm for AI, computer vision, and practical engineering with real-world impact.
  • Legal authorization to work in the country where you apply.
  • Direct experience with silicon vendors or hardware partners is a plus.
  • Experience across several embedded and edge ecosystems is a plus.
  • C++ experience, including deploying models on microcontrollers, is a plus.
  • Experience running CI on self-hosted hardware runners is a plus.
  • Contributions to open-source projects or developer ecosystems are a plus.
  • Experience working with international teams is a plus.
  • Willingness to travel to industry events is a plus.

Benefits

  • Equity package.
  • Flexible working hours.
  • 24 days of vacation.
  • Birthday leave.
  • Local public holidays.
  • New Apple MacBook Air or Pro.
  • Apple Studio Display.
  • AirPods Pro 3.
  • Dedicated budget for personal and professional development.
  • Work on advanced AI projects.
  • On-site collaboration in a global hub.

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