ML Data Engineer

Job Description What’s the opportunity?

We’re looking for a Machine Learning Data Engineer to enable data and ML capabilities that directly power the bank’s flagship Next Best Action (NBA) initiative, projected to deliver an incremental $160MM+ annual run-rate by FY28. As an ML Data Engineer, you will build and scale an AI-driven decisioning system that delivers hyper-personalized client experiences, ensuring the right action, at the right time, through the right channel, with the right offer, content, and placement.

You will build AI agents that accelerate action creation and automate decisioning at scale, driving meaningful impact across millions of client interactions. You will work end to end across the ML lifecycle, from data and features through to deployed, monitored, and continuously improving models, bridging cutting-edge research and production systems to deliver measurable, AI-driven value.

• Designing, building, and maintaining scalable data pipelines and feature stores that support end-to-end ML workflows for the NBA platform
• Collaborating with ML researchers and software engineers to product ionize models and translate experimental approaches into reliable, high-performing systems
• Developing and deploying AI agents that automate workflow, reduce manual processes, and accelerate business process
• Owning the ML data lifecycle, from data ingestion, validation, and feature engineering to deployment, monitoring, and continuous optimization
• Ensuring data quality, reliability, governance, and performance at scale while enabling hyper-personalized, real-time client experiences

• Bachelor’s degree in Computer Science, Software Engineering, or a related field, with 3+ years of professional experience as a data or software engineer
• Proficiency in Python and Java, with hands‑on experience using modern data and ML tooling (e.g., Spark, Airflow, feature stores, ML platforms)
• Strong foundation in both data and software engineering, designing and building scalable data pipelines and ML‑ready datasets in hybrid environments spanning on‑prem infrastructure and public cloud platforms (i.e. AWS)
• A solid understanding of the ML data lifecycle, including feature engineering, model integration, deployment support, and monitoring
• Experience building or enabling AI‑driven automation (e.g., agents, workflow orchestration, or decision engines) that reduces manual effort
• Experience with Dev Ops and CI/CD tooling such as Jenkins and Git Hub Actions to automate testing, builds, and deployments for data and ML pipelines
• Excellent collaboration and communication skills, with the ability to translate complex technical ideas into practical, business‑focused solutions

• Become part of a team that thinks progressively and works collaboratively. We care about seeing each other reach full potential.
• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock options where applicable.
• Leaders who support your development through coaching and managing opportunities.
• Ability to make a difference and lasting impact from a local‑to‑global scale.

RBC Borealis is the driving force behind Royal Bank of Canada’s AI and data innovation. As part of Canada’s largest financial institution, we bring together a team of architects, engineers, scientists, and product experts on a mission to revolutionize finance through world‑class research, solutions, and a resilient data platform. With locations across Toronto, Waterloo, Montreal, Calgary, and Vancouver, we’re at the forefront of AI research and platform development.

With a focus on cutting‑edge research in areas like time series forecasting, causal machine learning, and responsible AI, we are seamlessly integrating AI research and data engineering, to solve critical challenges in the financial industry. We are building intelligent, and scalable, data‑driven solutions that will help communities thrive and drive innovation for our customers across the bank.

RBC is an equal opportunity…