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Sr Machine Learning Engineer Jobs in Vancouver, BC

Weir

Senior Machine Learning Developer

Vancouver, BC · Hybrid

Senior Machine Learning Developer Vancouver - Hybrid Job Summary Shape the future of AI in mining by developing production-ready machine learning solutions that drive safer, smarter and more ...

Clio

Machine Learning Engineer

Vancouver, BC · Hybrid

CA$129K - CA$174K/yr

We are currently seeking a Machine Learning Engineer to join our rapidly growing engineering team. This role is for someone who is passionate about building innovative solutions and being exposed to ...

Workday, Inc.

Machine Learning Engineer

Vancouver, BC · On-site

$128 - $192/hr

About the Role As a Machine Learning Engineer on the AI Platform Context and Retrievals team, you will develop tailored user experiences using advanced Agentic AI, LLMs and RAG. You will collaborate ...

Workday

Machine Learning Engineer

Vancouver, BC · On-site

About the Role As a Machine Learning Engineer on the AI Platform Context and Retrievals team, you will develop tailored user experiences using advanced Agentic AI, LLMs and RAG. You will collaborate ...

SAP

Senior Machine Learning Scientist

Vancouver, BC · On-site

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 ...

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Sr Machine Learning Engineer information

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

AspectSr Machine Learning EngineerData Scientist
CredentialsBachelor's/Master's in CS, ML, or related fields; experience with ML frameworksBachelor's/Master's/PhD in CS, Statistics, or related fields; strong analytical skills
Work EnvironmentDevelops and deploys ML models, collaborates with engineering teamsAnalyzes data, builds models, interprets data insights for business
Industry UsageTech, finance, healthcare, e-commerceResearch, marketing, finance, tech

While both roles involve working with data and models, Sr Machine Learning Engineers focus on building and deploying scalable ML systems, whereas Data Scientists primarily analyze data and develop insights. The roles often overlap but differ in technical focus and responsibilities.

How does a Sr Machine Learning Engineer typically collaborate with data scientists and software engineers within a project team?

Sr Machine Learning Engineers frequently act as a bridge between data scientists, who focus on model development and experimentation, and software engineers, who handle system integration and production deployment. They translate prototype models into scalable, production-ready solutions, ensuring that models are optimized for real-world performance. Collaboration often involves reviewing code, aligning on data pipeline requirements, and participating in regular team meetings to address technical and business objectives. This cross-functional teamwork is essential for delivering reliable machine learning products.

What is a Sr Machine Learning Engineer?

Senior Machine Learning Engineers are experienced professionals who design, develop, and implement machine learning models and systems. They work on complex problems, lead technical projects, and often mentor junior engineers. Their responsibilities include data preprocessing, model selection, algorithm development, and optimizing solutions for scalability and performance. Senior ML Engineers also collaborate closely with data scientists, software engineers, and stakeholders to integrate machine learning into products and services.

What are the key skills and qualifications needed to thrive as a Sr Machine Learning Engineer?

To thrive as a Sr Machine Learning Engineer, you need advanced expertise in machine learning theory, programming (Python, R), data modeling, and a strong background in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, cloud platforms (AWS, GCP), and relevant certifications (like TensorFlow Developer) is highly beneficial. Strong problem-solving skills, effective communication, and the ability to lead and mentor teams set top candidates apart. These skills ensure the ability to design scalable ML solutions, collaborate effectively, and drive impactful business outcomes.
What are popular job titles related to Sr Machine Learning Engineer jobs in Vancouver, BC? For Sr Machine Learning Engineer jobs in Vancouver, BC, the most frequently searched job titles are:
What job categories do people searching Sr Machine Learning Engineer jobs in Vancouver, BC look for? The top searched job categories for Sr Machine Learning Engineer jobs in Vancouver, BC are:
Sr Machine Learning Engineer jobs near you
Infographic showing various Sr Machine Learning Engineer job openings in Vancouver, BC as of August 2026, with employment types broken down into 100% Full Time. Highlights an 66% In-person, and 34% Remote job distribution.

Senior Machine Learning Engineer

Starboard Recruitment

Vancouver, BC

$150K - $170K/yr

Full-time

Re-posted 22 days ago

Job description

Follow Starboard Recruitment on LinkedIn for ongoing job opportunities, market updates and advice: https://www.linkedin.com/company/starboard-recruitment
Opportunity is with one of Canada's fastest growing, well-funded, Series-B tech startups in the AI / ML domain.
Starboard Recruitment, on behalf of our client, is searching for an experienced Sr Machine Learning Engineer.
Our team will reach out to qualified candidates and discuss in further detail.
Key Responsibilities

  • AI Strategy Development – Partner with the Director of R&D to define and execute the company’s AI strategy, focusing on geoscientific applications.

  • Full-Cycle ML Leadership – Manage all aspects of the machine learning lifecycle, from data preprocessing to model deployment and performance monitoring, ensuring a streamlined and effective process.

  • Innovative ML Architectures – Design and implement a broad spectrum of machine learning solutions, spanning computer vision, time series forecasting, and geospatial data analysis, while integrating cutting-edge technologies and methodologies.

  • MLOps Best Practices – Drive the adoption of robust MLOps frameworks, including CI/CD pipelines for ML models, to enable smooth and scalable AI deployments.

  • AI Infrastructure & Optimization – Enhance AI infrastructure and workflows, focusing on performance, scalability, data pipeline efficiency, and automation across all ML processes.

  • Cross-Disciplinary Collaboration – Work closely with data engineers, scientists, and geoscientists to establish a well-integrated, end-to-end ML ecosystem within the company.

  • Continuous AI Advancement – Regularly improve the efficiency, reliability, and impact of AI-driven systems through iterative optimizations and refinements.

  • Geospatial ML Expertise – Familiarity with geospatial databases such as PostGIS and GeoPandas is highly desirable.

Qualifications

Experience:

  • At least 7 years of hands-on experience in machine learning engineering, with a strong record of successfully deploying ML solutions into production environments.

Technical Proficiency:

  • Expert-level Python programming skills and deep knowledge of ML frameworks, including PyTorch, scikit-learn, and inference engines like ONNX Runtime and OpenVINO.

  • Strong grasp of various ML algorithms, architectures, and their real-world applications.

  • Experience working with large-scale datasets and cloud computing environments, particularly AWS.

  • Proficiency in software engineering best practices, version control systems, and CI/CD methodologies.

  • Hands-on experience with containerization, orchestration, and microservices-based architectures.

  • Solid understanding of data security, privacy considerations, and compliance requirements in AI-driven applications.

Leadership & Soft Skills:

  • Proven ability to lead and mentor ML teams through complex projects.

  • Strong analytical and strategic thinking skills to solve challenging AI problems.

  • Exceptional communication skills, capable of conveying technical concepts to both technical teams and executive stakeholders.

  • Strong project management capabilities, with the ability to oversee multiple initiatives simultaneously.

  • Passion for continuous learning and adaptability in the ever-evolving field of machine learning.

Education:

  • Master’s or Ph.D. in Computer Science, Machine Learning, or a related discipline. Industry certifications and contributions to the ML community (such as research publications or open-source projects) are a strong plus.

Follow Starboard Recruitment on LinkedIn for ongoing job opportunities, market updates and advice: https://www.linkedin.com/company/starboard-recruitment