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# AI/ML Engineer specialized in Computer Vision for Embedded Systems (Edge AI)

- **Company:** Safran
- **Location:** Granada, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Profiling, Cyber Security, Continuous Integration, Data Files, Data Transformation, Software Debugging, Linux, Linux on Embedded Systems, Embedded Software, Python (Programming Language), Machine Learning, Object Detection, OpenCV, Release Management, Tensorflow, Software Deployment, Software Engineering, Management of Software Versions, Pytorch, Transfer Learning, Deep Learning, Model Validation, Data Strategy, Git, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, Software Version Control
- **Published:** October 31, 2026
- **Apply:** https://www.adzuna.es/details/5946679589

## About the Role

Bachelor's or master's degree in Computer Science, Artificial Intelligence, Telecommunications, Electronics, Software Engineering, or equivalent.

At least three years' professional experience developing computer-vision, machine-learning, or deep-learning solutions.

Hands-on PyTorch and/or TensorFlow experience in development, training, fine-tuning, and experimentation.

Experience training and fine-tuning image-classification and object-detection models, including YOLO architectures.

Dataset expertise: collection, annotation, augmentation, balancing, versioning, quality control, and train/validation/test splits.

Experience evaluating accuracy and robustness with suitable metrics, analysing errors, and improving models/datasets.

Experience optimising and quantising networks for edge deployment, including Qualcomm QNN.

Experience with Qualcomm AI Hub or equivalent conversion, compilation, profiling, and deployment toolchains.

Embedded Linux knowledge; practical AI deployment/debugging on Qualcomm QCS/QCM or comparable ARM-based edge platforms.

Proficiency in Python, Git, Linux, systematic debugging; strong analytical, communication, and professional English skills.

Desirable skills.

The company will positively consider knowledge and experience in:

Advanced object-detection architectures, recent YOLO variants, transfer learning, distillation, pruning, and quantisation-aware training.

Qualcomm QCS/QCM, AI Hub, QNN SDK, Hexagon DSP/HTP acceleration, and on-target performance profiling.

ONNX, TensorFlow Lite, OpenCV, model conversion, and heterogeneous CPU/GPU/DSP/NPU optimisation.

MLOps: experiment tracking, dataset/model versioning, reproducible training, continuous integration, automated evaluation, and model release management.

## Description

We are looking for an AI/ML Engineer specialized in Computer Vision for Embedded Systems (Edge AI) to develop vision for compact, battery-powered optronic products. The role covers data strategy, dataset generation, training, optimization, evaluation, deployment, and validation on Qualcomm QCS/QCM platforms.

Within the Optronics team, you will train and fine-tune classification and object-detection models, including YOLO, for efficient edge inference. You will quantize models with Qualcomm QNN, build deployment workflows using Qualcomm AI Hub, and integrate and validate models on embedded targets.

You combine strong computer vision and deep-learning expertise with practical embedded-systems knowledge. You use PyTorch and TensorFlow, design data-collection and annotation strategies, evaluate model quality and runtime performance, and collaborate with embedded software, hardware, systems, and product teams to deliver accurate, efficient, and maintainable AI capabilities for demanding field environments.

What you'll be doing:

Define data-collection strategies reflecting product use cases, environments, sensor configurations, and target classes.

Generate, curate, annotate, version, and quality-check image/video datasets for classification and object detection.

Train and fine-tune classification and detection models with PyTorch and TensorFlow, including YOLO detectors.

Design training/validation experiments, tune hyperparameters, analyse failures, and improve generalisation.

Evaluate accuracy, precision, recall, F1, mAP, latency, memory, power, and robustness.

Optimise and quantise networks with Qualcomm QNN, balancing quality, compute, memory, latency, and power.

Build and maintain Qualcomm AI Hub pipelines: conversion, compilation, profiling, validation, and release packaging.

Deploy and integrate models on Qualcomm QCS/QCM; troubleshoot preprocessing, inference, post-processing, and application interfaces.

Validate end-to-end vision performance on representative hardware, cameras, datasets, and operational scenarios.

Collaborate with embedded software, hardware, systems, verification, cybersecurity, and product teams; document reproducible workflows and test evidence.