
Alberta Machine Intelligence Institute grows Alberta’s AI and machine learning capacity and helps organizations adopt machine intelligence. It advances research in AI and machine learning and translates that research into industry adoption through education, training, and business advisory services. Amii operates as a non-profit national AI institute affiliated with the University of Alberta and runs research labs, applied projects, and workforce development programs. The institute works with industry partners across sectors to build in-house AI capabilities and scale academic advances into commercial and operational use.

Alberta Machine Intelligence Institute grows Alberta’s AI and machine learning capacity and helps organizations adopt machine intelligence. It advances research in AI and machine learning and translates that research into industry adoption through education, training, and business advisory services. Amii operates as a non-profit national AI institute affiliated with the University of Alberta and runs research labs, applied projects, and workforce development programs. The institute works with industry partners across sectors to build in-house AI capabilities and scale academic advances into commercial and operational use.
“If you are interested in the application of artificial intelligence (AI) and machine learning (ML) methods for Energy systems optimization, Distributed Energy Resources, and Multi-Agent RL, 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.”
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, T.rex AI, 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.
The client’s core team is small, so the resident will have a chance to become one of the company’s first hires and fundamentally contribute to the company’s future success. This role will have the opportunity to not only express and grow a technical skillset, but also learn how a company is built from the ground up, and how a deeply technical product makes its way from vision to scale.
About The Client T.rex AI is a deeptech startup founded in 2021 by three UofA graduate students to commercialize their joint research.
The company’s main product is ALEX, a Deep Reinforcement Learning agent that helps electric utilities unlock grid capacity without requiring infrastructure upgrades. ALEX is pre-trained on a client utility’s historical data in a digital twin environment. It is deployed as a containerized runtime on AMI 2.0 smart meters, where it provides premise level load forecasting, flexibility forecasting and orchestration services to the customer utility.
2026 will be a pivotal year for T.rex AI, as the company will hire its first employees, attempt to scale ALEX’s ML pipeline by a factor of ~1k in order to execute on pilot projects and deploy the first agents.
About The Project In technical terms, an ALEX agent is a Deep Reinforcement Learning policy trained in a custom environment using a premise's historical energy usage data. The agent's purpose is to perform orchestration: scheduling of the premise's load flexibility assets (Batteries, Electric Vehicles). The agent's reward is derived from a local energy market (LEM), where connected agents can exchange energy at a dynamically determined price that correlates with the LEM's supply and demand ratio. The agent's observation space includes premise-specific time-series information (e.g., load demand, flexibility asset status) and shared observations (daytime, market statistics from LEM’s last settled round). This frames the orchestration task as a multi-agent game in a partially observable environment, where maximizing reward requires successful resource arbitrage. ALEX's policy neural network shares parameters between the actor, critic, and a parallel-trained world model that provides forecasting services.
The first pilot in 2024 trained ALEX agents for several LEMs, each treated as an independent environment with ~20 premises, via PPO and a basic form of Centralized Learning / Decentralized Execution (CLDE) and self-play. Execution of the 2026 deliverables requires developing the capability to train ALEX agents for ~20,000 premises simultaneously.
While the 2024 setup produced competent agents it also faces scalability challenges. PPO’s principal scalability challenges with available computational resources has been addressed through a switch to IMPALA. A more fundamental issue is that each environment’s population had to independently rediscover basic principles (e.g., discharging batteries when the premise needs energy, daily load cycles). This is the challenge this project aims to solve.
The Goal Is To Directly Reduce Per-agent Walltime By Mitigating The Need For Per-environment Rediscovery Of Basic Game Rules. Possible Avenues For This Include
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
Required Qualifications
Preferred Qualifications
Non-Technical Requirements
Why You Should Apply
Besides Gaining Industry Experience, Additional Perks Include
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 date of January 28, 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 and the role. 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.