Fast Facts
This is a paid residency opportunity for a Machine Learning Resident at T.rex AI, focusing on AI and ML for energy systems optimization, with potential for permanent employment after 12 months. You'll work alongside top scientists, engage in impactful projects, and develop cutting-edge machine learning models.
Responsibilities: Design and optimize models for energy consumption forecasting, work with large datasets, utilize advanced ML techniques, and collaborate with stakeholders on MVP solutions.
Skills: Solid background in machine learning, deep reinforcement learning, proficiency in Python and ML frameworks, understanding of classical statistics, and familiarity with Linux and Git.
Qualifications: Completion of a MSc. or PhD in Computer Science specializing in reinforcement learning; experience with deep reinforcement learning and strong software engineering skills are preferred.
Location: Edmonton, Alberta, Canada
Compensation: Not provided by employer. Typical compensation ranges for this position are between CAD 70,000 - CAD 100,000.
“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.”
-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, 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:
- Scaling CLDE across multiple local energy markets
- Curriculum-based learning approaches
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 models for energy consumption forecasting tasks.
- Work with large datasets for training, fine-tuning, and validating models.
- Utilize state-of-the-art modeling techniques and ML frameworks, tools and open-source libraries to enhance model performance, accelerate workflows, and optimize data processing.
- Undertake applied research on deep reinforcement learning and time-series analysis techniques to expand the capabilities of the current 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 with specialization in reinforcement learning.
- Proficient in developing, training and evaluating deep reinforcement learning agents.
- 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 time-series or energy consumption data.
- Prior experience with multi-agent deep reinforcement learning.
- 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:
- 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 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.
