Fast Facts
Join BigGeo as a Machine Learning Resident for a 12-month paid program focused on geospatial data analysis, working alongside top minds in AI to develop cutting-edge systems.
Responsibilities: Design and implement workflows for geospatial data analysis, optimize ML pipelines, and engage with stakeholders to develop client-focused solutions.
Skills: Strong background in machine learning, particularly with agentic workflows and large-scale geospatial datasets. Proficient in Python and ML frameworks such as TensorFlow and PyTorch.
Qualifications: Completion of a graduate degree in Computer Science or related field, ideally with experience in LLM/VLMs and machine learning model implementation.
Location: Edmonton, Alberta, Canada
Compensation: Not provided by employer. Typical compensation ranges for this position are between $70,000 - $110,000.
“If you are interested in the application of machine learning and foundation models for geospatial data analysis, this is the right opportunity for you. Be a part of a team of research and machine learning scientists building agentic, multimodal systems for large-scale geospatial reasoning from the ground up and get mentored by some of the best minds in AI during the process.” - Mara Cairo, Product Owner, Advanced Technology
Description
About the Role
This is a paid residency that will be undertaken over a 12-month period in Calgary with the potential to be hired by our client, BigGeo, 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
BigGeo is redefining spatial intelligence with an AI-ready Discrete Global Grid System (DGGS) that transforms how spatial data is captured, indexed, and monetized. The BigGeo platform powers mission-critical decisions across sectors where location intelligence drives outcomes—from large-scale infrastructure projects and environmental planning to logistics and emergency response. BigGeo is industry agnostic, unlocking possibilities for organizations that have yet to realize the value a system like BigGeo’s can deliver.
About the Project
BigGeo is undertaking development of an AI integration layer that will comprehensively understand the full scope of our spatial cloud capabilities—from core platform features and compute functions to available datasets and market opportunities. The system will synthesize knowledge across our function library, data catalog, product specifications, and marketing materials to dynamically match customer business challenges with relevant BigGeo solutions, accelerating discovery and reducing time-to-value. This integration will enable both internal teams and external customers to query BigGeo's capabilities using natural language, receiving contextually relevant recommendations that combine the right data, compute functions, and platform features to solve specific spatial intelligence problems. By continuously learning from usage patterns and marketplace activity, the AI will identify emerging use cases and solution opportunities that drive commercial growth while ensuring customers quickly find the optimal path to their desired outcomes.
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 are seeking a talented and motivated individual with a strong background in machine learning, particularly in developing agentic workflows with LLMs and VLMs to leverage large-scale geospatial datasets for answering diverse analytical questions.
Key Responsibilities:
- Design, implement, optimize, and evaluate agentic workflows (with tool use) to extract value from large multi-modal geospatial datasets.
- Utilize state-of-the-art agentic frameworks, tools and libraries to enhance model performance, accelerate workflows, and optimize data processing.
- Undertake applied research on ML and LLM/VLM fine-tuning techniques to address the limitations in existing models.
- Optimize ML pipelines to ensure efficiency, scalability, and real-time processing capabilities.
- 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.
Required Qualifications:
- Completion of a Computer Science (or a related graduate degree program) MSc. or PhD, ideally with experience in LLM/VLMs.
- Proficient in implementing, fine-tuning and evaluating machine learning and deep neural network models.
- Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., Scikit-learn, TensorFlow, PyTorch, OpenCV, Pandas, HuggingFace).
- Solid understanding of classical statistics and its application in model validation.
- Familiarity with Linux, Git version control, and writing clean code.
- A positive attitude towards learning and understanding a new applied domain .
- Must be legally eligible to work in Canada.
Preferred Qualifications:
- Familiarity with and hands-on experience with multi-modal geospatial data.
- Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
- Experience/familiarity with software engineering best practices.
- Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus.
Non-Technical Requirements:
- 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.
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 January 27, 2025 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.
