Spécialiste apprentissage machine/Machine Learning Specialist - Device Analytics

Team and role

Responsibilities

• Design, train, and optimize deep neural networks for image classification, regression, detection, and re-identification.
• Develop architectures for real-time inference, including on hardware with limited computing resources.
• Evaluate machine learning approaches against performance, compute, and hardware constraints.
• Contribute to data acquisition and annotation, model training and evaluation, optimization, and product integration.
• Improve tools for model training, retraining, testing, and deployment.
• Evaluate technologies and components, recommend approaches, and integrate appropriate solutions.
• Build and test proofs of concept and prototypes for future product development.

Qualifications and expertise

• Master’s degree in electrical engineering, computer engineering, computer science, physics, mathematics, or a related field, with relevant machine learning specialization or experience.
• At least 3 years of hands-on deep learning experience; practical model development and deployment experience is ideal.
• Strong applied research, experimentation, problem-solving, and scientific literature review skills.
• Fluent spoken and written French and English; the role involves international collaborators and clients.
• Excellent Python skills; experience with at least one object-oriented language such as C++, C#, or Java.
• Practical experience with at least one of PyTorch, TensorFlow, or Keras, and good command of Git and software development practices.
• Independence, initiative, multidisciplinary collaboration, and ability to explain complex technical concepts clearly.

Additional assets

• Experience with object detection, classification, or recognition; object tracking; image processing; or computer vision is an asset.
• Experience with Docker, cloud environments, embedded systems, resource-constrained computing, or Linux is an asset.

Benefits

• Benefits include a compensation program, training-cost reimbursement, flexible work hours, subsidized meals, free coffee and fruit, free employee parking, and relaxation spaces.