ML Software Engineer — Build Scalable Financial ML Systems

RBC; Vancouver; On-site; CAD 120,000 - 160,000; Full time

Job summary

RBC Borealis seeks a Software Engineer specialized in Machine Learning to own end-to-end ML projects from data prep to deployment. You will work with researchers to build scalable pipelines, and apply best practices across the full software lifecycle. Collaboration with product and research teams is essential.

The role emphasizes delivering robust ML systems, documentation, and technical advisory, with a focus on production-ready solutions in a fast-paced financial services environment.

Qualifications

• Experience building modular and robust software systems in Python or similar language.
• Experience across the full software development lifecycle including testing, coding standards and code reviews.
• Experience across ML lifecycle from prototyping to production.
• Experience building microservices, data pipelines and using relational and non-relational databases.
• Experience with data science tooling and deep learning frameworks.
• Experience with DevOps engineering (CI/CD pipelines, observability, containers).

Responsibilities

• To build cutting edge ML solutions throughout the research and product development lifecycle.
• To play a key role in the design and development of Borealis’ machine learning products.
• To partner with RBC Borealis’s research and product teams to ensure the seamless delivery of these products.
• To apply engineering and data best practices to build robust and scalable large-scale machine learning software systems.
• To support projects with thorough documentation, design decisions, and technical advisory.

Skills

Python

SDLC best practices

ML lifecycle

Microservices

Data pipelines

Databases

Deep learning

DevOps

Tools

CI/CD

Containers

Job description

RBC Borealis seeks a Software Engineer specialized in Machine Learning to own end-to-end ML projects from data prep to deployment. You will work with researchers to build scalable pipelines, and apply best practices across the full software lifecycle. Collaboration with product and research teams is essential.

The role emphasizes delivering robust ML systems, documentation, and technical advisory, with a focus on production-ready solutions in a fast-paced financial services environment.