“If you are interested in the application of machine learning, natural language processing, document intelligence, and computer vision for automating complex land and geospatial data, this is the right opportunity for you. Be a part of the team of research and machine learning scientists building a multi-modal AI-driven intelligence layer to transform complex source data into structured, trusted, and decision-ready data products from the ground up, and get mentored by some of the best minds in AI during the process.”
- Amor Provins, Product Owner, Advanced Technology
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.
This is a paid residency that will be undertaken over a 12-month period with the potential to be hired by our client, Altalis, 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.
Altalis is Alberta’s trusted source for spatial data and imagery. Established in 1998, Altalis has been an authoritative provider of provincial mapping data for more than 25 years, supporting government, municipalities, utilities, industry, consultants, and other organizations that rely on accurate land and geospatial information. Our work supports land management, energy, environmental monitoring, infrastructure planning, and many other sectors that rely on accurate and current spatial information.
Through a joint venture agreement with Alberta Data Partnerships Ltd., Altalis is responsible for the day to day management and distribution of key digital mapping datasets. Altalis helps ensure that mapping products are available, accessible, accurate, and affordable. The company plays a leading role in the management, maintenance, updating, storage, distribution, and value added redistribution of primary provincial mapping datasets.
Altalis delivers spatial data through Altalis.com, a modern webstore powered by AWS that enables secure, reliable, and efficient access to both paid and open data products. Customers can explore, view, and acquire spatial data with ease. Altalis is also known for its strong customer service, practical data expertise, and long standing relationships with clients across Alberta.
Altalis is exploring how applied machine learning, computer vision, natural language processing, and large language models can transform complex land and geospatial information into structured, searchable, and decision-ready data products.
The project focuses on building an AI-driven intelligence layer to support the extraction and interpretation of registered interests, such as Rights of Way, Caveats, and Easements, from land title certificates, registered survey plans, spatial layers, records, and related business rules. These high-value datasets are often difficult to interpret, standardize, and scale using manual processes alone.
This work will evaluate and prototype AI-assisted approaches for extracting, organizing, validating, and linking important legal and geospatial information from complex visual-text source materials. Potential techniques include document understanding, information extraction, entity recognition, relationship mapping, deep learning vectorization, large language models, and human-in-the-loop validation workflows.
As a Machine Learning Resident, you will support the design and development of an end-to-end document intelligence system that combines modern machine learning methods with traditional computational and document analysis techniques. Working with Altalis, Amii, and Altalis’s domain experts, the Resident will help shape practical, reliable, multi-modal extraction capabilities that reduce manual review time, improve consistency, and support the creation of a unified, queryable, province-wide dataset.
The goal is to improve the efficiency, quality, and scalability of data creation while maintaining the accuracy and trust required for authoritative land and spatial information. This applied AI project has real product potential and will support future data products for government, municipalities, industry, consultants, and other organizations that depend on reliable land and geospatial information.
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, specifically in natural language processing, Document Intelligence, Computer Vision (CV), Large Language Models (LLMs), Optical Character Recognition (OCR) or related information extraction techniques, and applied machine learning for complex data workflows.
- Design, implement, optimize, and evaluate multi-modal ML frameworks for textual, graphical, and geospatial information extraction, classification, and data structuring from complex, unstructured PDFs and scanned documents.
- Prepare, curate, annotate, and preprocess high-quality datasets for training, fine-tuning, benchmarking and evaluating models.
- Develop and integrate a robust Text Extraction Pipeline utilizing OCR, Layout Detection, name entity recognition and LLM/VLM based reasoning to parse complex legal text and geospatial entities.
- Develop and integrate a Graphical Extraction Pipeline utilizing deep learning models for map and plan interpretation, including image enhancement, raster-to-vector conversion, spatial OCR with Oriented Bounding Boxes (OBB), and pixel-based semantic segmentation.
- Undertake applied research on ML and natural language processing, information extraction, and human in the loop validation techniques to address the limitations in existing models.
- Optimize ML pipelines to ensure efficiency, scalability, and integration with downstream Geographic Information Systems (GIS).
- Collaborate with the project team and stakeholders to develop MVP and client focused solutions.
- Engage in regular client meetings, contributing to presentations and reports on project progress
- Completion of a Computer Science (or a related graduate degree program) MSc. or PhD with specialization in Computer Vision, Natural Language Processing, Document AI, Multi-modal learning or Optical Character Recognition applications.
- Hands-on experience developing, training, fine-tuning, and evaluating machine learning and deep neural network models in PyTorch and/or TensorFlow.
- Experience with OCR, document layout analysis, image processing, object detection, semantic segmentation, and multi-modal model development.
- Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., OpenCV, HuggingFace, Scikit-learn, Pandas).
- Solid understanding of classical statistics and its application in model validation.
- Familiarity with Linux, Git version control, and writing clean and reproducible code.
- A positive attitude towards learning and understanding a new applied domain.
- Must be legally eligible to work in Canada.
- Familiarity with and hands-on experience with complex text, document based, or tabular data, spatial data, cadastral mapping, or Geographic Information Systems (GIS).
- Familiarity with geospatial data, cadastral mapping, or Geographic Information Systems (GIS) and land related data workflows.
- Experience with deep learning-based vectorization and semantic segmentation.
- Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
- Experience/familiarity with software engineering best practices and deploying models in production environments.
- Demonstrated ownership of complex problems and strong 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.
Besides gaining industry experience, additional perks include:
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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)
If this sounds like the opportunity you've been waiting for, please don’t wait for the closing June 26, 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.
Please visit https://www.amii.ca/ for more information
