Machine Learning Resident

"If you are interested in the application of artificial intelligence (AI) and machine learning (ML) methods for energy consumption modeling and forecasting, this is the right opportunity for you. Be a part of the team of research and machine learning scientists building a state-of-the-art predictive model 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 with the potential to be hired by our client, Enerva, 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

Enerva Energy Solutions is a 100% Canadian, employee-owned energy efficiency and decarbonization firm dedicated to the mission of optimizing energy costs and reducing global emissions. As the largest Canadian-owned program delivery company in our sector, we provide a homegrown alternative to American private equity-backed firms. Our team of 50+ engineers, analysts, and software developers delivers end-to-end energy programs nationwide. At Enerva, we don't just consult—we deliver the technical solutions and innovative strategies needed to power a greener, more resilient future.

About the Project

This project builds on prior validation work exploring how data-driven approaches can improve the way organizations understand and forecast energy consumption across diverse facilities. The goal is to enhance existing methods used to track, plan, and manage energy use by developing more adaptable and forward-looking analytical tools that reflect real-world operational conditions.

Working alongside an experienced industry partner, the project will focus on learning from historical facility data to identify patterns, trends, and drivers of energy use over time. Rather than relying on a single predefined solution, the work is exploratory and iterative, allowing insights from the data to inform direction as the project progresses. Emphasis is placed on producing practical, interpretable outcomes that can support budgeting, performance tracking, and proactive energy management across different facility types.

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, specifically time-series analysis and forecasting.

Key Responsibilities:

• Design, implement, optimize, and evaluate machine learning models to support energy consumption forecasting and related analytical tasks.
• Prepare, clean, and preprocess high-quality datasets to ensure they are suitable for training or fine-tuning, validating, and comparing forecasting models.
• Apply state-of-the-art modeling techniques, ML frameworks, tools and open-source libraries to improve model performance, accelerate workflows, and optimize data processing.  
• Undertake applied research on ML and time-series  techniques to improve or extend existing forecasting approaches.
• Contribute to improving  ML pipelines with a focus on  efficiency, scalability, and real-time processing capabilities.
• Collaborate with the project team and stakeholders to develop proof-of-concept and MVP-level solutions aligned with the client. 
• 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 with specialization in time-series analysis, or energy forecasting applications.
• Proficient in developing, training, fine-tuning, and evaluating machine learning and deep neural network models in PyTorch and/or TensorFlow, including model tuning and performance optimization.
• Proficient in Python and common ML frameworks, libraries, and toolkits (e.g., Scikit-learn, LMStudio, TensorFlow, PyTorch, OpenCV, Pandas, HuggingFace), including data cleaning, preprocessing, and feature engineering for modeling workflows.
• Solid understanding of classical statistics and its application in model evaluation, validation, and performance assessment.
• Familiarity with Linux, Git version control, and writing clean code.
• A positive attitude towards learning and applying machine learning techniques in a new applied domain.
• Must be legally eligible to work in Canada.

Preferred Qualifications:

• Familiarity with and hands-on experience with time-series or energy consumption data.
• Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
• Experience/familiarity with software engineering best practices in an applied or research setting.
• 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 demonstrate 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 2, 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.