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Data Annotation Engineer Jobs in Vancouver, BC (NOW HIRING)

Dialpad

AI Evaluation Engineer

Vancouver, BC · On-site

Your role As an AI Evaluation Engineer, you'll be an integral part of our AI Evaluation team ... You will create, configure, and monitor data annotation jobs to keep evaluation and calibration ...

Prenuvo

Senior AI Applied Scientist II

Vancouver, BC · On-site

Drive data quality standards, clinical annotation criteria, and knowledge grounding (e.g., RAG ... Partner across clinical, annotation, and ML Engineering teams, raising the code-quality bar and ...

All JobsData Annotation Engineer JobsData Annotation Engineer Jobs in Vancouver, BC

Data Annotation Engineer information

See Vancouver, BC salary details

$9

$29

$68

How much do data annotation engineer jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for data annotation engineer in Vancouver, BC is $29.66, according to ZipRecruiter salary data. Most workers in this role earn between $17.15 and $35.27 per hour, depending on experience, location, and employer.

What is a data annotation engineer?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What are the key skills and qualifications needed to thrive as a data annotation engineer?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

What are the main challenges faced by data annotation engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What is the salary of data annotation engineer?

The salary of a data annotation engineer typically ranges from $40,000 to $80,000 annually, depending on experience, location, and the complexity of annotation tasks. Entry-level positions may start lower, while experienced professionals with specialized skills in tools like Labelbox or CVAT can earn higher salaries.

What job categories do people searching Data Annotation Engineer jobs in Vancouver, BC look for?

The top searched job categories for Data Annotation Engineer jobs in Vancouver, BC are:

Data Annotation Engineer jobs near you
Infographic showing various Data Annotation Engineer job openings in Vancouver, BC as of August 2026, with employment types broken down into 1% As Needed, 68% Full Time, 29% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $61,699 per year, or $29.7 per hour.

AI Evaluation Engineer

Vancouver, BC • On-site

Dialpad
Telecommunications • 1 - 5K employees
Learn more about Dialpadexternal

Full-time

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

Your role
As an AI Evaluation Engineer, you'll be an integral part of our AI Evaluation team, owning evaluation coverage for Dialpad's Agentic AI systems alongside our existing evaluation lead. A key focus will be co-owning LLM-judge metric development and calibration, scenario and benchmark dataset curation, and structured error analysis to support release-readiness decisions for our agentic voice and chat solutions.

This position reports to the manager of the AI Evaluation team and has the opportunity to be based in our Vancouver office.

What you'll do 

  • You will design and execute validation strategies for agentic, NLP, and speech workflows across staging, beta, and release candidates.
  • You will build, run, and improve regression evaluations, A/B comparisons, and red teaming analyses to determine whether product and model changes are ready to move forward.
  • You will co-own LLM-judge metric development, calibration, and prompt refinement across evaluation dimensions.
  • You will create, configure, and monitor data annotation jobs to keep evaluation and calibration datasets fed on schedule.
  • You will develop and maintain QA tooling, notebooks, and pipeline components that make recurring evaluations scalable and reusable across teams.
  • You will investigate bugs, triage issues, and decide whether problems should become engineering escalations, test set additions, or follow-up analysis.
  • You will collaborate with cross-functional teams, including applied science, engineering, and Product QA.

Skills you'll bring 

  • Bachelor's or Master's degree in Computer Science, Software Engineering, Computational Linguistics, or a related field.
  • 3+ years of experience in QA, test engineering, model evaluation, or applied ML quality for AI-driven products.
  • Experience designing structured test strategies across manual and automated workflows.
  • Comfort working with complex AI systems such as speech, NLP, LLM, or agentic products.
  • Experience working with evaluation datasets, gold sets, adversarial test sets, or benchmark creation for AI systems.
  • Strong analytical skills for investigating failures, comparing outputs, and identifying actionable quality patterns.
  • Experience collaborating with cross-functional technical teams and communicating clearly through documentation and reporting.

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