“If you are interested in applying machine learning (ML) to real-world industrial challenges in aerospace – particularly optimizing decisions for parts allocation and maintenance operations through data analytics and explainable ML - this is a perfect opportunity for you. Be a part of a team of research and machine learning scientists and get mentored by some of the best minds in AI while doing it. ”
- Xu Han, Machine Learning Scientist
About The Role
This is a paid Residency that will be undertaken over a 6-month period. The Resident will be reporting to an Amii Scientist and regularly consult with the Pratt & Whitney Canada team to share insights and engage in knowledge transfer activities. Also, please note that the top candidate(s) will be subject to a security check, and the results of which will be shared with Pratt & Whitney Canada.
About Our Client
Pratt & Whitney Canada (P&WC) is a global leader in the aerospace industry, headquartered in Longueuil, Quebec. We manufacture next-generation engines that power the world’s largest fleet of business, general aviation, and regional aircraft and helicopters. For nearly 100 years, we have pioneered advancements in engine development, supporting cargo and equipment, transportation, wildfire suppression, and passenger travel.
About The Project
While the project has a lot of different branches, the strongest focus will be on developing a tool to schedule different activities in the production flow. The solution must be custom-tailored to the needs of different departments and needs to be able to optimize based on multiple different factors. It is crucial that the tool is properly documented to account for potential changes in priorities and focus. Analytics of historical data will be the foundation of the optimization.
Who You Are
You have completed a graduate-level program or higher (M.Sc./Ph.D.) in Computing Science, AI/ML, Engineering, or a related field, with substantial research or project experience in ML and optimization - particularly in forecasting, predictive modeling, time series analysis. You possess strong Machine Learning Engineering (MLE) skills to drive end-to-end development and deployment of AI solutions. You have a deep understanding of metrics and evaluation practices, along with the capacity to contextualize scientific metrics for business applications. You are proficient in Python and familiar with key ML frameworks and libraries such as Scikit-learn, TensorFlow, PyTorch, and Pandas. Your positive attitude toward learning new applied domains, especially in aerospace or manufacturing, and your ability to communicate technical concepts clearly make you a valuable team player who is enthusiastic about collaborating across interdisciplinary teams.
What You Will Be Doing
In this role, you will be instrumental in developing, refining, and operationalizing high-quality, end-to-end AI products for Pratt & Whitney Canada. Your work will focus on creating predictive and prescriptive models for various AI modules (such as cost optimization, work scheduling, parts allocation, procurement, and inventory management) using real-world data. You will explore and implement a range of ML and optimization techniques, including supervised and unsupervised learning, forecasting, and classification models. You will be responsible for data preprocessing, including handling missing data, noise reduction, and synchronization issues. You will also productionize AI solutions on existing enterprise data infrastructure using MLOps tools. Furthermore, you will help establish a strong AI governance framework compliant with P&WC security and AI best practices, support the P&WC AI team in refining and industrializing initial models, and participate in the experimentation and piloting of developed solutions. You will collaborate with interdisciplinary teams, participate in project meetings, and contribute to reports on model performance and project milestones. Your efforts will directly contribute to enhancing Pratt & Whitney Canada's maintenance strategies, operational efficiency, and data-driven decision-making.
Required Skills / Expertise
We are looking for a talented and enthusiastic individual with solid knowledge of ML, strong MLE principles for end-to-end development and deployment, and demonstrated experience in applied settings. Expertise in forecasting, predictive modeling, time series analysis, and optimization is crucial. You should have a deep understanding of evaluation metrics and the capacity to contextualize scientific metrics for business needs.
Key Responsibilities
- Develop, refine, and operationalize high-quality AI products, including predictive models and optimization models for operational decision-making across different AI modules.
- Perform effective exploratory data analysis (EDA) using data analytics and visualization approaches.
- Build, evaluate, and maintain production ML models to predict operational states and supply needs.
- Formulate and solve complex decision problems across equipment maintenance, parts allocation, procurement, and inventory management using state-of-the-art prescriptive optimization methods.
- Package models into modular microservices with automated pipelines (CI/CD, batch/real-time inference) to continuously drive end-to-end operational decision-making in production.
- Prepare and curate high-quality datasets from diverse sources (including event sequences and textual data) for model training and validation.
- Utilize state-of-the-art ML frameworks and tools (e.g., NumPy, Scikit-learn, SciPy, Pandas, PyTorch) to achieve optimal model performance and streamline data processing.
- Collaborate with cross-functional teams to build and deploy AI solutions that address client needs, ensuring seamless integration into existing systems and supporting the industrialization of models.
- Establish and maintain a strong AI governance framework compliant with P&WC security and AI best practices.
- Participate in the experimentation, piloting, and continuous improvement of developed AI solutions.
- Engage in regular client meetings, contributing insights and updates on model performance and project milestones through presentations and detailed reports.
Required Qualifications
- Completion of a graduate-level program or higher (M.Sc./Ph.D.) in Computing Science, Mathematics, Operational Research, Machine Learning, Industrial Engineering, or equivalent.
- Research or project experience solving real world problems using optimization, time series analysis, and predictive modeling techniques, with a preference for demand forecasting and cost optimization use cases.
- Strong foundation in applied ML, statistics, and optimization.
- Strong software development practices
- Deep understanding of classical tabular ML (classification, regression, clustering, PCA, supervised learning, unsupervised learning), forecasting models (e.g., XGBoost/LightGBM, SVM, MLP, ARIMA, Prophet, DeepAR), survival analysis, and probabilistic modeling.
- Hands-on experience with Linear Programming and Mixed-Integer Programming.
- Hands-on experience performing data analytics and data processing with large datasets.
- Strong understanding and application of Machine Learning Engineering (MLE) and Software Engineering (SWE) principles for end-to-end model development and deployment.
- Proficiency in the Python programming language and related ML frameworks, libraries, and toolkits (e.g., Scikit-learn, PyTorch, Pandas, SciPy, PuLP).
- Comfortable using MLOps tools (e.g., Databricks, MLflow, ZenML, Kedro) in a cloud environment for CI/CD.
- Basic knowledge of SQL.
- A positive attitude toward learning and understanding new applied domains.
- Deep understanding of evaluation metrics and practices, with the capacity to contextualize scientific metrics for business needs.
- Must be legally eligible to work in Canada.
Preferred Qualifications
- Previous experience applying ML to time series data for predictive maintenance problems, particularly in aerospace or manufacturing.
- Understanding of explainable ML and SHAP analysis.
- Previous experience with a broad range of optimization algorithms, including Evolutionary Algorithms, Simulated Annealing, and Particle Swarm Optimization.
- Good understanding of Reinforcement Learning, Model Predictive Control, Monte Carlo Simulation, and Discrete Event Simulation (DES).
- Some experience with classical NLP methods for text extraction.
- Experience deploying ML models in production environments.
- Good MLE or software engineering skills.
- Strong understanding of deep learning, including CNNs, RNNs, LSTMs, and Transformers.
- Familiarity with event sequence data characteristics and their challenges in an industrial context.
Non-Technical Requirements
- Desire to take ownership of a problem and demonstrated 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 Fellow and Amii Scientist for the duration of the project
- Participate in professional development activities
- Gain access to the Amii community and events
- 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 October 12, 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.
