ABOUT US

Sunia Technology Inc. is an industry leading technology company specializing in AI technology for handwriting, hand-drawing, as well as image and video processing on mobile and portable devices. We are currently seeking a dedicated, diligent, and self-driven Machine Learning Engineer to become a valuable addition to our R&D team.

REQUIREMENTS：

• Master or PhD degrees in Computer Science, Electrical Engineering or equivalent.
• 3+ years of experience in machine learning with a strong focus on computer vision and/or LLMs.
• Deep understanding of deep learning architectures: CNNs, transformers, diffusion models, and attention mechanisms.
• Hands-on experience with frameworks like PyTorch, TensorFlow, Transformers, OpenCV, and similar.
• Solid software engineering skills – version control, unit testing, CI/CD, containerization (Docker, Kubernetes).
• Strong programming skills in one of the programming languages such as C++, Python, etc.
• Excellent problem-solving and communication skills.
• First class analytical, diagnostic and problem-solving skills.
• Excellent verbal and written communication skills with an ability to collaborate with other team members.
• Passion to work in an exciting environment and deliver new technologies and products to the marketplace.
• Ability to learn quickly.
• A positive mental attitude and growth mindset.
• Please advise in your application whether you are eligible to work in Canada.

• Design, develop, and deploy machine learning models with a focus on computer vision and natural language processing.
• Work with large-scale visual and textual datasets to train and fine-tune state-of-the-art models.
• Integrate computer vision and LLM models into production-grade applications and pipelines.
• Collaborate cross-functionally with product managers, data scientists, and backend engineers to define AI-driven product features.
• Stay current with research in deep learning, vision-language models, multimodal AI, and foundation models.
• Optimize model performance and inference time for scalable deployment.