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Machine Learning Engineer Jobs in British Columbia

BDO Canada

Senior Machine Learning Engineer

Vancouver, BC · On-site

CA$84K - CA$128K/yr

Advanced programming skills in Python, with practical experience using popular machine learning libraries such as scikit-learn, TensorFlow, and/or PyTorch. Capable of building, tuning, and deploying ...

Ubc

Machine Learning Engineer/Scientist

Vancouver, BC

CA$6K - CA$8K/mo

Staff - Non Union Job Category M&P - AAPS Job Profile AAPS Salaried - Scientific Eng., Level A Job Title Machine Learning Engineer/Scientist Department Human Resources Support | Department of ...

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Machine Learning Engineer information

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

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.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

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.

More about Machine Learning Engineer jobs
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 job categories do people searching Machine Learning Engineer jobs in British Columbia look for? The top searched job categories for Machine Learning Engineer jobs in British Columbia 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:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in British Columbia as of June 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Senior Machine Learning Engineer

Starboard Recruitment

Vancouver, BC • On-site

$150K - $170K/yr

Full-time

Posted 26 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