Machine Learning Resident Client AI (1 year term)

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 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 clients core team is small so the resident will have a chance to become one of the companys first hires and fundamentally contribute to the companys 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

AI is a deeptech startup founded in 2021 by three UofA graduate students to commercialize their joint research.

The companys 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 utilitys 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 AI as the company will hire its first employees attempt to scale ALEXs 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 premises historical energy usage data. The agents purpose is to perform orchestration: scheduling of the premises load flexibility assets (Batteries Electric Vehicles). The agents reward is derived from a local energy market (LEM) where connected agents can exchange energy at a dynamically determined price that correlates with the LEMs supply and demand ratio. The agents observation space includes premise-specific time-series information (e.g. load demand flexibility asset status) and shared observations (daytime market statistics from LEMs 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. ALEXs 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 20000 premises simultaneously.

While the 2024 setup produced competent agents it also faces scalability challenges. PPOs principal scalability challenges with available computational resources has been addressed through a switch to IMPALA. A more fundamental issue is that each environments 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 Youll be presented with opportunities to both personally and professionally develop as you build your career. Were 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 clients organization at the end of the term (at the clients discretion)

About Amii

One of Canadas 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 worlds 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 youve been waiting for please dont wait for the closing date of January 28 2026 to apply. Were 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 youd be a fit for Amii and the 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 wont be used in the selection process.