REQUIRED QUALIFICATIONS

• Master’s degree (or equivalent) in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Industrial Engineering or a related field.
• Proficient in Python and comfortable working with cloud platforms or high-performance computing environments.
• Experience in developing and validating AI/ML models using PyTorch, TensorFlow, OpenCV.
• Practical experience using deep learning architectures (YOLO, U-Net, DETR, RFROI-CNN, Pix2Pix) for both object detection and image segmentation tasks.
• Hands-on involvement in object detection under degraded or noisy visual conditions, including spectrogram-based signal data.
• Familiarity with computer vision techniques, data augmentation and deep learning model evaluation.
• Knowledge of benchmarking methodologies and performance metrics.
• Demonstrated experience in evaluating or fine-tuning AI systems.
• Good communication skills.
• Fluent in English and French, both written and oral.
• Ability to work independently and as part of a multidisciplinary team.

PREFERRED SKILLS

• Motivated to work in RDI environments, ideally in AI or ML domains, particularly in applied machine learning for computer vision and signal/image processing tasks or related roles.
• Interest or experience in standards, auditing, or certification frameworks for AI.
• Motivated to deepen skills in MLOps and AI system deployment in research and industrial settings.
• Motivated to expand CI/CD skills and integrate more advanced tooling

• Design, train, and evaluate machine learning models for computer vision and signal/image processing tasks, such as semantic segmentation, object detection, and/or tracking; experience with defense-oriented applications of ML is considered a plus.
• Apply deep learning techniques for object detection in challenging environments, including scenarios with low signal-to-noise ratio (SNR), high noise levels, camouflage, or occlusions. Experience with models such as, e.g., YOLO, DETR, Pix2Pix, etc., is a plus.
• Prototype AI solutions involving object detection, generative models (e.g., GANs), and/or large language models (LLMs), with a strong focus on metrics, ranging from system-level measurements, like latency, autonomy, etc., up to explainability or robustness.
• Investigate the use of generative models for data augmentation and simulation, with an emphasis on AI benchmarking.
• Contribute to the construction and validation of annotated datasets, including class-aware data splitting and label quality control.
• Build and maintain scripts and frameworks for dataset preparation, metrics tracking, model testing, and result visualization.
• Develop and apply benchmarking methodologies to assess model performance, robustness, and explainability across diverse scenarios, including hardware-oriented metrics (e.g., Edge AI deployment, latency measurement).
• Contribute to the evaluation and benchmarking of machine learning models based on multiple criteria, such as performance, explainability, factual robustness, human oversight, and fairness.
• Actively contribute to the development and maintenance of evaluation pipelines, toolkits, and reproducible workflows.
• Assist in deploying and optimizing machine learning models in both local and cloud environments.
• Develop and maintain model versioning, monitoring, and governance frameworks; MLOps experience is a plus.
• Troubleshoot issues related to model performance, deployment, and infrastructure reliability.
• Collaborate with data scientists, software engineers, and DevOps teams to streamline AI/ML workflows.
• Collaborate on experiments aimed at solving real-world challenges.
• Support project planning, execution, and reporting within collaborative research, development, and innovation (RDI) projects.
• Write technical documentation and research reports; contribute to scientific dissemination through conferences, workshops, and whitepapers. Participate in knowledge transfer and standardization efforts related to AI model assessment and the development of trustworthy AI systems.