About The Project This project focuses on developing generative AI techniques for video inpainting in stereo conversion, leveraging diffusion models and spatiotemporal architectures to reconstruct missing or occluded regions in monocular video. In stereo conversion workflows, generating a second view from a single camera feed often results in occluded regions that need to be inpainted with depth-aware, temporally consistent information.

Unlike traditional methods that rely on heuristic propagation or adversarial learning, this research explores diffusion-based generative models to synthesize high-fidelity missing regions while maintaining structural, temporal, and depth consistency.

Who Are You

You have completed a graduate-level program or higher (M.Sc/Ph.D) in Computing Science, Machine Learning, or Engineering, with substantial research and project experience in deep learning, computer vision, and generative AI. You are proficient in Python and experienced with key ML frameworks and libraries such as PyTorch, Hugging Face Diffusers, vLLM serving and inference, and OpenCV. Your positive attitude towards learning new applied domains and your ability to communicate technical concepts clearly make you a valuable team player. You are enthusiastic about collaborating across interdisciplinary teams to achieve excellence.

What You Will Be Doing

In this role, you will develop GenAI computer vision models specifically tailored for Outsyders’ immersive technology applications to enhance interactive experiences, contributing directly to the advancement of Outsyders’ products and services.

Your responsibilities include preparing and curating relevant datasets, utilizing commercial and open-source models and tools to support GenAI computer vision tasks, and optimizing model pipelines to ensure scalability and real-time processing. You will collaborate with cross-functional teams to develop client-centric solutions, participate in client meetings, and contribute to reports on project progress.

Required Skills / Expertise We’re looking for a talented and enthusiastic individual with solid knowledge of machine learning, deep learning, and computer vision.

Key Responsibilities

• Design, implement, and optimize diffusion-based generative models for visual effects, mainly focussing on video inpainting and masking, while maintaining structural, temporal, and depth consistency. Refine solutions for industry-specific challenges.
• Prepare, curate, and preprocess high-quality large scale datasets for training or fine-tuning, and validating models, integrating diverse sources such as images, videos, and 3D assets for visual effects and content creation.
• Leverage state-of-the-art computer vision and GenAI frameworks and tools, including both commercial and open-source libraries to enhance model performance, accelerate workflows, and optimize data processing.
• Collaborate with cross-functional teams to build and deploy computer vision-based minimum viable products (MVPs) that address client needs, ensuring seamless integration into existing systems.
• Engage in regular client meetings, contributing insights and updates on model performance and project milestones through presentations and detailed reports.
• Optimize computer vision pipelines to ensure efficient, scalable, and real-time inference capabilities, leveraging techniques like model quantization, transfer learning, and distributed processing.

Required Qualifications

• Completion of a graduate level program or higher (M.Sc/Ph.D) in Computing Science, Machine Learning or Engineering
• Research and project experience in diffusion models, computer vision, and generative AI
• Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., PyTorch, Ray, Hugging Face Diffusers, Lightning, and OpenCV)
• A positive attitude towards learning and understanding a new applied domain
• Must be legally eligible to work in Canada

Preferred Qualifications

• Publication record in peer-reviewed academic conferences or relevant journals in Machine Learning and Computer Vision
• Experience with GenAI vision models, especially Video Diffusion models
• Experience with large-scale distributed training and cloud platforms, including GCP.
• Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus

Non-Technical Requirements

• Desire to take ownership of a problem and demonstrated leadership skills
• Interdisciplinary team player enthusiastic about working together to achieve excellence
• Capable of critical and independent thought
• Able to communicate technical concepts clearly and advise on the application of machine intelligence
• Intellectual curiosity and the desire to learn new things, techniques, and technologies

Why You Should Apply

Besides Gaining Industry Experience, Additional Perks Include

• Work under the mentorship of an Amii Fellow and Amii Scientist for the duration of the project
• Participate in professional development activities
• Gain access to the Amii community and events
• Build your professional network
• The opportunity for an ongoing machine learning role at the client’s organization at the end of the term (at the client’s discretion)