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Full Time Machine Learning Data Annotation 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 ... and data-driven innovation? Join Weir Motion Metrics and make a lasting impact on some of North ...

Remitly

Senior Machine Learning Engineer

Burnaby, BC · On-site

CA$168K - CA$210K/yr

Partner with data scientists, product owners, and engineers across verticals to turn prototypes into reliable, customer-facing ML systems. * Optimize models and pipelines using MLOps best practices ...

MasterCard

Lead Data Scientist

Vancouver, BC · Hybrid

... ready machine learning solutions. This is a full-time hybrid position based in Toronto, Canada ... Relevant experience in data science, AI, or machine learning roles, with proven ability to own and ...

Variational AI

Chemical data scientist

Vancouver, BC · Remote

VANCOUVER, BC (OR REMOTE IN CANADA) / FULL TIME Variational AI is radically accelerating the ... machine learning. Traditional approaches to small molecule drug discovery require over ten years ...

Teck

Data Scientist III

Vancouver, BC · Hybrid

CA$107K - CA$132K/yr

Design, develop, and deploy machine learning models, advanced analytics, and Generative AI ... Regular Full Time | Location: Vancouver | Workplace Type: #LI - Hybrid

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All JobsMachine Learning Data Annotation JobsFull Time Machine Learning Data Annotation JobsFull Time Machine Learning Data Annotation Jobs in Vancouver, BC

Full Time Machine Learning Data Annotation information

What is a full time machine learning data annotation job?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What are the key skills and qualifications needed to thrive as a full time machine learning data annotation specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are the most commonly searched types of Machine Learning Data Annotation jobs in Vancouver, BC?

The most popular types of Machine Learning Data Annotation jobs in Vancouver, BC are:

What are popular job titles related to Full Time Machine Learning Data Annotation jobs in Vancouver, BC?

For Full Time Machine Learning Data Annotation jobs in Vancouver, BC, the most frequently searched job titles are:

What job categories do people searching Full Time Machine Learning Data Annotation jobs in Vancouver, BC look for?

The top searched job categories for Full Time Machine Learning Data Annotation jobs in Vancouver, BC are:

Full Time Machine Learning Data Annotation jobs near you
Infographic showing various Full Time Machine Learning Data Annotation job openings in Vancouver, BC as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

Senior Machine Learning Engineer

Starboard Recruitment

Vancouver, BC

$150K - $170K/yr

Full-time

Re-posted 11 hours 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