Student, AI/ML Engineer (Winter 2027, 8 Months)

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We are a purpose-driven, dynamic and sustainable pension plan. An industry leading global investor with teams in Toronto to London, New York, Singapore, Sydney and other major cities across North America and Europe. We embody the values of our 665,000 members, placing their best interests at the heart of everything we do.

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The AI Delivery and Innovation team is seeking a fourth-year undergrad looking to jumpstart their career as an AI/Machine Learning Engineer. Our team helps create paved roads for the development of high-quality, secure and well-governed AI solutions by providing effective machine learning platforms & tooling. The co-op student must demonstrate their ability to build and evaluate machine learning solutions, showing proficiency in Python, SQL and applied ML concepts, by completing a technical challenge we will provide.

The student should come in with a foundational grasp of machine learning concepts and solid Python skills, built through coursework or personal projects, and a genuine curiosity about how models move from a notebook into a real production system. Candidates must have an interest in the investment industry and enterprise technology, since the work will touch platforms that support our investment and operational teams.

You will be responsible for:

• Explore, clean, and prepare data, working hands-on with SQL and structured datasets

• Build and evaluate machine learning models across a range of techniques, with guidance on experiment design

• Support feature engineering and model development alongside the team's engineers

• Work on Generative AI use cases, such as prompting and retrieval-based methods, under supervision

• Document your work and share findings in a way that's clear to both technical and non-technical audiences

Preferred Skills & Experience

• Currently a fourth-year undergrad in Computer Science, Engineering, Math, Data Science, or a related program

• Demonstrate knowledge of core machine learning concepts: supervised and unsupervised learning, model evaluation, feature engineering

• Proficient in Python and its data stack (NumPy, pandas, scikit-learn), with a solid grasp of data structures, algorithms, and object-oriented programming

• Good understanding of database concepts and able to write SQL to work with structured data

• Coursework or project exposure to large language models and generative AI (prompting, embeddings, RAG basics)

• Must be interested in taking models beyond the notebook and building the components that put them into production

Nice to haves

• Exposure to deep learning frameworks such as PyTorch or TensorFlow

• Hands-on experience with LLM tooling such as Hugging Face, LangChain, or vector databases

• Skilled in MLOps practices: CI/CD pipelines, containerization, automated testing, or data pipelines

• Able to use cloud-native data and ML services (Azure, AWS, or GCP)

• Project management aptitude and proficiency with tools like Azure DevOps

You may also be eligible to receive an annual Incentive Award pursuant to our Short-term Incentive plan and our Long-Term Incentive plan (if applicable), and to participate in our group benefits and retirement plans - details on these elements of compensation are included within OMERS & Oxford offer letters.

As one of Canada’s largest defined benefit pension plans, our people-first culture is at its best when our workforce reflects the communities where we live and work - and the members we proudly serve.

From hire to retire, we are an equal opportunity employer committed to an inclusive, barrier-free recruitment and selection process that extends all the way through your employee experience. This sense of belonging and connection is cultivated up, down and across our global organization thanks to our vast network of Employee Resource Groups with executive leader sponsorship, our Purpose@Work committee and employee recognition programs.

Artificial intelligence (AI) tools are used to support certain stages of the OMERS recruitment process. While AI assists us in our process, human judgment and decision-making remain central to our candidate experience.