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Machine Learning Engineer Jobs in British Columbia

We are currently seeking a Manager, Machine Learning Engineering to join our rapidly growing engineering organization. This role is for someone who is passionate about building innovative solutions ...

Work with interdisciplinary teams of developers, designers, and business experts to develop ... Machine Learning engineering practices. You will act as a technical partner and lead by example ...

We're forming small, senior, cross-functional AI teams that bring together product leaders, machine learning engineers, and full-stack builders to create intelligent agents used by millions of people ...

Its patented unsupervised machine learning technology, advanced device intelligence, powerful ... As platform engineers, we are building a next-generation machine learning platform, which ...

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Machine Learning Engineer information

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in British Columbia? The most popular types of Machine Learning Engineer jobs in British Columbia are:
What are popular job titles related to Machine Learning Engineer jobs in British Columbia? For Machine Learning Engineer jobs in British Columbia, the most frequently searched job titles are:
What cities in British Columbia are hiring for Machine Learning Engineer jobs? Cities in British Columbia with the most Machine Learning Engineer job openings:
What are popular job titles related to Machine Learning Engineer jobs in BC? For Machine Learning Engineer jobs in BC, the most frequently searched job titles are:
Infographic showing various Machine Learning Engineer job openings in British Columbia as of August 2026, with employment types broken down into 78% Full Time, and 22% Contract. Highlights an 100% In-person job distribution.

Staff Machine Learning Software Engineer

Royal Bank of Canada

Vancouver, BC

Full-time

Posted 18 days ago


Job description

Job Description

RBC Borealis is seeking an experienced Staff Machine Learning Software Engineer to leads the strategic planning, execution, and delivery of advanced AI initiatives in Borealis AI, aligning with organizational goals and driving impactful solutions to address complex business challenges. In this role, you will direct the management of multiple teams in AI Engineering, focusing on strategy implementation.

Responsibilities Include

Develop and oversee implementation of strategic AI roadmaps aligned with organizational objectives and deliver innovative solutions for complex business challenges.
Direct cross functional collaboration, building strong relationships to drive impactful outcomes and influence enterprise strategies.
Lead multiple AI focused initiatives, ensuring operational efficiency, resource optimization, and alignment with corporate strategic plans.
Provide strategic direction and leadership to teams, ensuring successful execution of high impact AI projects and contributing to long term objectives.
Manage AI Engineering initiatives of significant complexity, applying advanced technical expertise to solve abstract problems, and making independent decisions across business units.
Make independent decisions on program, technical or operational strategy for the department, providing leadership, coaching and mentorship through senior managers and managers. Set strategic direction for the department or area receiving high level direction from senior leaders.
Drive innovation across areas of responsibility identifying opportunities for making operational changes and practices, solving highly complex problems broadly related to AI Engineering.
Lead cross functional collaboration efforts, fostering strong internal relationships across the organization and external relationships to drive business outcomes.

You're our ideal candidate if you have:

  • Bachelor's degree in Computer Science, Statistics, Math, Engineering or a related technical field;

  • 10+ years of experience leading high-impact machine learning solutions in a product-centric environment;

  • Experience managing/mentoring high-performing teams of data scientists and engineers;

  • Proven expertise across the research and development lifecycle, from prototyping to production, with strong ability to engage stakeholders;

  • Deep expertise in machine learning, statistics, and data science with experience in optimization, A/B testing, and causal inference;

  • Possess a deep expertise in production ML infrastructure including model serving platforms, feature stores, and data pipelines, with ability to support teams in deploying and maintaining ML models at scale.

  • Exceptional communication skills and ability to translate complex concepts to diverse audiences.

  • Demonstrated a mastery of end-to-end ML system ownership in production environments, including model design, feature engineering, model architecture, drift detection, and serving infrastructure at scale.

  • A deep expertise in building systems that serve as shared ML platforms for multiple downstream teams and use cases.

  • A proven ability to lead cross-functional technical initiatives by coordinating with diverse stakeholder groups (feature providers, policy teams, client teams) while maintaining system integrity and translating technical results to non-technical audiences.

About the AI Group:
RBC's AI Group is the AI accelerator for RBC, with a focus on driving the shift from early-stage AI projects to scaled, client outcomes that amplify the impact of RBC's people. In addition to helping scale the biggest AI opportunities at RBC, the AI Group is responsible for advancing research into emerging use cases across generative and agentic AI, while maintaining expertise in security, responsible AI and regulatory expectations. The Business Enablement function within the AI Group partners with LOBs and Functions to set AI ambition, originate transformation opportunities, and frame programs for delivery - ensuring RBC remains at the frontier of AI-enabled value creation.


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.


#LI-POST
#TECHPJ

Job Skills

Big Data Analytics, Client Counseling, Coaching Others, Critical Thinking, Decision Making, Industry Knowledge, Machine Learning (ML), Results-Oriented, Software Engineering, Software Product Design

Additional Job Details

Address:

777 BAY ST, TH 27: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:

2026-07-22

Application Deadline:

2026-08-26

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

Our Employment Opportunities

At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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Employment Type: FULL_TIME