ML Data Engineer

RBC; Toronto; On-site; CAD 60,000 - 80,000; Full time

Job summary

A leading financial institution is seeking a Machine Learning Data Engineer in Toronto. You will build and scale AI-driven systems to enhance client experiences and automate decision-making. This role involves managing the ML data lifecycle, collaborating on model production, and ensuring data quality. Candidates should have a Bachelor's degree in Computer Science and experience in Python, Java, and data engineering tools. A strong understanding of the ML data lifecycle is essential. Join a progressive team focused on impactful innovations.

Benefits

Comprehensive Total Rewards Program

Bonuses and flexible benefits

Supportive leadership

Qualifications

• 3+ years of professional experience as a data or software engineer.
• Hands-on experience using modern data and ML tooling.
• Experience with DevOps and CI/CD tooling like Jenkins.

Responsibilities

• Design and maintain scalable data pipelines for ML workflows.
• Collaborate to productionize models and create high-performing systems.
• Ensure data quality and governance in real-time client experiences.

Skills

Proficiency in Python

Proficiency in Java

Experience with Spark

Experience with Airflow

Collaboration

Data Engineering

Education

Bachelor’s degree in Computer Science or Software Engineering

Tools

AWS

Apache Kafka

Snowflake

Job description

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.

Your responsibilities include:

• 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 productionize 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

You’re our ideal candidate if you have:

• 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 DevOps and CI/CD tooling such as Jenkins and GitHub 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

What’s in it for you?

• 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.

About RBC Borealis

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.

Inclusion and Equal Opportunity Employment

RBC is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veterans status, Aboriginal/Native American status or any other legally‑protected factors. Disability‑related accommodations during the application process are available upon request.

Job Skills

AI Agents, AI Ops, Amazon SageMaker, Apache Hadoop, Apache Kafka, Apache Spark, AWS Cloud Computing, AWS Glue, Business Requirements Analysis, Collaboration, Database Development, Data Engineering, Data Pipelines, Data Warehousing (DW), DevOps, End‑to‑End Testing, ETL Processing, Feature Engineering, Generative AI, Hybrid Cloud Computing, Snowflake (Platform), Software Requirements Analysis

Additional Job Details

Address: RBC WATERPARK PLACE, 88 QUEENS QUAY W:TORONTO

City: Toronto

Country: Canada

Work hours/week: 37.5

Employment Type: Full time

Platform: TECHNOLOGY AND OPERATIONS

Job Type: Regular

Pay Type: Salaried

Posted Date: 2026-02-11

Application Deadline: 2026-05-04

Note

Applications will be accepted until 11:59 PM on the day prior to the application deadline date above.

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RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.