Machine Learning Resident - Client: Snapshot (12 month term)

“If you're excited about building real-world computer vision systems that operate in challenging industrial environments, this is the perfect opportunity for you. Join a team of research and machine learning scientists developing deployable edge AI solutions for industrial inspection, computer vision, and predictive maintenance while being mentored by some of the best minds in applied AI” - Maithrreye, Machine Learning Scientist 

About the Role

This is a paid Residency that will be undertaken over a twelve-month period with the potential to be hired by our client, Snapshot, afterwards (note: at the discretion of the client). The Resident will report to an Amii Scientist and regularly consult with the client team to share insights and engage in knowledge transfer activities.

Successful candidates will be members of a cross-functional project team with backgrounds in ML research, project management, software engineering, and new product development. This is a rare opportunity to be mentored by world-class scientists and to develop something truly impactful.

About the Client

Snapshot is building edge-enabled intelligent monitoring solutions for the energy sector. Its platform combines high-resolution machine vision cameras, thermal imaging, acoustic sensors, and vibration sensors deployed at remote industrial sites to continuously monitor equipment health. By transforming multimodal sensor data into actionable insights, Snapshot aims to automate inspections, reduce manual intervention, enable predictive maintenance, and minimize costly equipment downtime across thousands of industrial assets.

About the Project

The Snapshot Residency focuses on developing robust computer vision and edge AI solutions for automated industrial inspection in remote heavy oil facilities. The primary objective is to accurately detect and read analog gauges from high-resolution camera feeds, converting visual measurements into structured digital data that supports equipment health monitoring and predictive maintenance. In addition to analog gauge reading, other initiatives will explore the use of machine vision, along with the other multi-model sensors deployed, to infer other information about the condition of assets on site or variations in the process away from normal operating conditions. A key challenge is developing models that perform reliably under challenging real-world conditions, including varying illumination, shadows, vibration, motion blur, changing viewing angles, snow, dust, and partial occlusions. Initially, the project will focus on a small set of industrial gauge types to develop production-ready solutions before expanding to broader industrial assets. The residency will also investigate scalable data-centric AI approaches, including automated dataset annotation using Vision-Language Models (VLMs) to reduce manual labeling effort, while exploring lightweight computer vision architectures optimized for accurate, low-latency deployment on resource-constrained edge devices. 

Required Skills / Expertise

Are you passionate about building great solutions? You’ll be presented with opportunities to both personally and professionally develop as you build your career. We’re looking for a talented and enthusiastic individual with a solid background in machine learning, computer vision, along with proven experience in applied settings.

Key Responsibilities:

• Design and develop computer vision pipelines for industrial analog gauge detection, localization, and value extraction.
• Build robust object detection, keypoint estimation, and angle estimation models capable of accurately reading multiple gauge types under challenging environmental conditions.
• Develop automated annotation pipelines using Vision-Language Models (VLMs) and active learning to efficiently label large-scale image datasets.
• Investigate temporal modeling techniques to distinguish true equipment state changes from camera vibration and environmental noise.
• Benchmark lightweight computer vision architectures (e.g., YOLO, RT-DETR, Nano models) for low-latency edge deployment.
• Evaluate model robustness under varying lighting conditions, shadows, weather effects, occlusions, image noise, and camera movement.
• Optimize models using quantization, pruning, TensorRT, ONNX, or other inference acceleration techniques for deployment on edge hardware.
• Collaborate with the project team and client to develop production-ready computer vision solutions and present experimental findings.
• Design and develop computer vision pipelines for other operationally interesting aspects such as identifying safety risks on site, operator compliance with SOP’s, etc. 

Required Qualifications: 

• Completion of an MSc or PhD in Computer Science or a related discipline specializing in Computer Vision, Machine Learning, Robotics, or Artificial Intelligence.
• Research or industry experience in computer vision, object detection, image segmentation, keypoint detection, or vision-based measurement systems.
• Experience developing deep learning models using PyTorch, OpenCV, Ultralytics YOLO, Detectron2, MMDetection, or similar frameworks.
• Strong programming skills in Python and familiarity with Linux, Git, and modern machine learning development practices.
• Interest in solving real-world industrial AI problems.

Preferred Qualifications:

• Experience with industrial vision applications, edge AI, embedded inference, or IoT systems.
• Experience with Vision-Language Models (VLMs) for automated annotation, zero-shot learning, or synthetic data generation.
• Experience deploying optimized models using TensorRT, ONNX Runtime, NVIDIA Jetson, OpenVINO, or related inference frameworks.
• Knowledge of model compression techniques including quantization, pruning, and knowledge distillation.
• Experience working with multimodal sensor data (vision, thermal imaging, vibration, or acoustic signals).
• Publication record in computer vision, machine learning, or applied AI.
• Experience working with rotating equipment such as hydraulic pumps/motor and internal combustion engines.

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 Scientist for the duration of the project
• Participate in professional development activities
• Gain access to the Amii community and events
• Get paid for your work (a fair and equitable rate of pay will be negotiated at the time of offer)
• 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)

About Amii

One of Canada’s three main institutes for artificial intelligence (AI) and machine learning, our world-renowned researchers drive fundamental and applied research at the University of Alberta (and other academic institutions), training some of the world’s top scientific talent. Our cross-functional teams work collaboratively with Alberta-based businesses and organizations to build AI capacity and translate scientific advancement into industry adoption and economic impact.

How to Apply

If this sounds like the opportunity you've been waiting for, please don’t wait for the closing September 30, 2026 to apply - we’re excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes! When sending your application, please send your resume and cover letter indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and won’t be used in the selection process.