Codify deployment patterns and contribute to internal tooling so each mandate makesthe platform stronger and the next deployment ships faster Support data collection and annotation efforts at ...
Codify deployment patterns and contribute to internal tooling so each mandate makesthe platform stronger and the next deployment ships faster Support data collection and annotation efforts at ...
... Support data collection and annotation efforts at customer sites when needed What We Are Looking For Must-Haves • 6+ years of experience combining production software engineering with ...
Quick apply
... Support data collection and annotation efforts at customer sites when needed What We Are Looking For Must-Haves • 6+ years of experience combining production software engineering with ...
Create interactive data viewers for images/video streams and metadata (sampling, filtering, dataset slices, annotation QA). Develop profiling utilities and performance dashboards (hot-path analysis ...
Quick apply
Create interactive data viewers for images/video streams and metadata (sampling, filtering, dataset slices, annotation QA). Develop profiling utilities and performance dashboards (hot-path analysis ...
Research Lead / Principal Scientist & Manager Post-Training Alignment Reinforcement Learning ...
Montreal, QC · On-site +1
Rather than relying solely on human preference data, we can ground reinforcement learning in the ... Drive human-in-the-loop evaluation with high annotation quality and sound scientific methodology
Research Lead / Principal Scientist & Manager Post-Training Alignment Reinforcement Learning ...
Montreal, QC · On-site +1
Rather than relying solely on human preference data, we can ground reinforcement learning in the ... Drive human-in-the-loop evaluation with high annotation quality and sound scientific methodology
Data Annotation information
See Quebec salary details
$9.13 - $13.22
4% of jobs
$16.50 is the 25th percentile. Wages below this are outliers.
$13.22 - $17.31
26% of jobs
The median wage is $19.75 / hr.
$17.31 - $21.39
33% of jobs
$24.77 is the 75th percentile. Wages above this are outliers.
$21.39 - $25.48
14% of jobs
$25.48 - $29.57
2% of jobs
$29.57 - $33.65
12% of jobs
$33.65 - $37.74
8% of jobs
$37.74 - $41.83
0% of jobs
$41.83 - $45.91
0% of jobs
$45.91 - $50
0% of jobs
$50 - $54.09
1% of jobs
$9
$25
$54
How much do data annotation jobs pay per hour?
As of Jun 12, 2026, the average hourly pay for data annotation in Quebec is $25.23, according to ZipRecruiter salary data. Most workers in this role earn between $17.07 and $30.05 per hour, depending on experience, location, and employer.
Is data annotation a legitimate?
Data annotation is a legitimate job that involves labeling data such as images, text, or audio to help train machine learning models. It is commonly performed remotely and requires attention to detail, basic technical skills, and familiarity with annotation tools. Many companies hire data annotators as part of their AI development teams.
What does a typical workday look like for someone in a Data Annotation role?
A typical workday as a Data Annotator involves reviewing datasets—such as images, audio, text, or video—and accurately labeling or categorizing information according to specific project guidelines. Most Data Annotators work independently, but they often collaborate with project managers or data scientists to clarify requirements and resolve ambiguities. Tasks may be repetitive, but adhering to precise standards is vital for maintaining data quality. Work environments can range from technology companies to remote or freelance settings, and advancement opportunities exist as team leads or quality assurance specialists for those who excel in consistency and reliability.
What is a Data Annotation job?
A Data Annotation job involves labeling and categorizing data, such as text, images, audio, or video, to help train machine learning models. Annotators apply tags, bounding boxes, or classifications to data based on specific guidelines. This process improves the accuracy of AI systems in recognizing patterns and making predictions. Many data annotation jobs require attention to detail and familiarity with specific domains. It is commonly used in applications like autonomous driving, natural language processing, and computer vision.
What are the key skills and qualifications needed to thrive in the Data Annotation position, and why are they important?
To thrive in Data Annotation, you need strong attention to detail, accuracy, and basic data handling skills, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data labeling software, or content management systems is frequently required, though specific certifications are rare. Excellent communication, time management, and the ability to focus on repetitive tasks distinguish top performers in this role. These skills are crucial because accurate and consistent data annotation directly impacts the quality of machine learning models and AI applications.
What does a data annotator do?
A data annotator labels and tags data such as images, text, or videos to help machine learning models understand and learn from the data. They use tools and follow guidelines to ensure accuracy and consistency, often working with large datasets in a structured environment. Attention to detail and knowledge of annotation tools are important for this role.
Do people actually make money on data annotation?
Data annotation jobs can provide a source of income, with pay rates varying based on the complexity of tasks, platform, and experience. Many annotators earn hourly or per-task wages, but earnings often depend on the volume of work completed and the employer's pay structure.
Is it hard to get hired for data annotation?
Getting hired for a data annotation role generally depends on the employer's requirements, such as attention to detail and basic computer skills. Many positions are entry-level and may not require prior experience or certifications, making them accessible to a wide range of applicants. However, competition can vary based on the number of available jobs and the quality of applicants.
What job categories do people searching Data Annotation jobs in Quebec look for? The top searched job categories for Data Annotation jobs in Quebec are:
Other
Posted 6 days ago
Job description
The Role
As a Forward Deployed Engineer, you embed with our most strategic Quebecmanufacturing accounts and own the full lifecycle of our AI deployments. You are the primarytechnical contact for the customer, a trusted advisor who codes side-by-side with their operationsand IT teams, and the two-way translator between shop-floor reality and our product roadmap.
You operate through the FDE lifecycle:
Phase 1 - Scoping. You land in the customer's environment and map their systems,stakeholders, and pain points
You run discovery directly with operators, controls teams, IT,and quality engineers.
Phase 2 - Build & Integration. You build, deploy, and iterate the AI solution end-to-end:
data pipelines, edge model deployment, OT integration, and data flywheel maturation.
Phase 3 - Production & Handover. You harden the deployment, document thearchitecture and customer political map, and transfer the account to existing LTS
team
You then rotate to your next account's Phase 1.
You ship code, not slide decks. You're measured on outcomes - production AI that actually runs atthe customer - not billable hours or generic features.
You collaborate closely with the AI Team (model training, MLOps, data exploration) and feed fieldsignals back to Product and Engineering so each mandate makes the platform stronger and the
next deployment ships faster.
You report to the Technical Project Manager (Quebec) within the Project Delivery Team. Typicalallocation: ~50% code, ~30% client (calls, on-site sessions, requirement gathering), ~20% scoping
and project documentation.
Travel: Up to 25% for on-site commissioning, deep discovery, troubleshooting, and customerrelationship building.
What You Will Do
Customer Deployment and OT Integration
Embed with two to four Quebec manufacturing accounts as their primary technical contactfor AI deployments
Lead end-to-end deployments of AI vision systems at customer facilities - fromshop-floor scoping through production handoff
Integratewith the customer's full operational stack: industrial communicationprotocols (OPC-UA, Modbus TCP, PLCs), edge AI inference (NVIDIA Jetson), customer ERP,and customer cloud data environment
Own the deployment lifecycle: software configuration, system validation, integrationtesting, and production handoff with a formal handover artifact for the LTS team
Troubleshoot software, networking, and integration issues in live production environments
Document deployment configurations, system behaviors, and best practices
Technical Customer Engagement
Serve as the primary technical point of contact during and after deployment
Train customer operators and engineers on-site and remotely, in French and English
Participate in occasional pre-sales calls and scoping sessions alongside the sales team
Translate AI value to non-specialists: model performance, accuracy thresholds, ROI inbusiness terms
Build working relationships with customer technical leads for long-term adoption
Solution Development and Data Flywheel
Build, deploy, and iterate production AI deployments end-to-end - you own the customerside data flywheel: edge cloud data capture, trained-model deployment to edge,production inference monitoring, and the feedback loop with the client
Shape the core product roadmap - your field experience directly informs what we buildnext: stability improvements, new platform capabilities, and reusable solutions that scaleacross all customers
Custom work you do at one customer often becomes a standardfeature for the next.
Codify deployment patterns and contribute to internal tooling so each mandate makesthe platform stronger and the next deployment ships faster
Support data collection and annotation efforts at customer sites when needed
What We Are Looking ForMust-Haves
6+ years of experience combining production software engineering with industrialautomation and/or applied AI
Strong production Python and proven track record shipping systems to customerinfrastructure - you have deployed real systems that run in front of real users, not justprototypes
Hands-on experience with industrial communication and/or edge AI deployment - atleast one of: OPC-UA / Modbus / PLC integration, Jetson or equivalent edge platforms,GenICam / industrial vision systems
Cloud experience - comfortable deploying and operating services in cloud environments(AWS / Azure / GCP)
Strong software fundamentals: Python, Linux, Docker, Git, comfort deploying to edgehardware
Solid networking fundamentals (TCP/IP, VLANs, firewalls) as they apply to industrialdeployments
Customer-facing seniority - you can hold the line in a discovery workshop with operators,an architecture review with the IT/OT director, and an executive briefing with the plant
manager, in the same week
Bilingual French and English - you can run a discovery workshop in French and write atechnical design document in English without losing precision
High agency, bias for action - you operate well in ambiguity and ship production code oncustomer infrastructure
Nice-to-Haves
Direct experience deploying AI/ML models in production on customer infrastructure
Industrial automation experience at large (PLC integration, controls, manufacturing
systems)
Industrial vision: GenICam / GigE cameras (Basler, Lucid, Cognex, Keyence), OpenCV,optical intuition (lens selection, lighting, specular reflection mitigation)
Specific ERP integration: SAP, Oracle, or similar
Jetson AGX (flash, BSP, Docker edge, embedded Linux)
AI/ML inference pipelines and real-time systems
Background in mobile robotics, drones, ROV/AUV, or remotely piloted vehicles - shares thesystems / embedded / perception / field-integration DNA
Background in food and beverage, CPG, automotive, packaging, or wood processing
Experience in a startup or high-growth environment where you have worn multiple hats
Who Thrives Here
You are comfortable operating in ambiguity. You can walk into a customer facility, read the room,understand what matters to their operation, and start solving problems without waiting to be told
exactly what to do. You have a high bar for what "working" actually means.You are technically credible across software, AI, and industrial systems, even if your depth skewsone direction
You know enough about ML to have a real conversation about model performanceand drift, and enough about OT to own integration with PLCs and ERPs. You learn fast and ask good
questions. You care about the customer outcome, not just task completion.
Logistics
Based in Quebec, 100% remote - you work from home anywhere in Quebec (GreaterMontreal, Quebec City, Sherbrooke, Trois-Rivieres, or elsewhere)
Proximity tomanufacturing customers required for periodic on-site work.
Up to 25% travel for on-site commissioning, deep discovery, troubleshooting, andcustomer relationship building
Bilingual French and English required
Mid-Senior individual contributor role reporting to the Technical Project Manager(Quebec) within the Project Delivery Team
Compensation
Competitive compensation and benefits. Cursor / Claude Code subscription included - we expectyou to use AI in your daily workflow.
WhyUs
Work on AI systems that have direct, measurable impact on real manufacturing operations
Join a technically deep team at a stage where your contributions are visible and your growthis real
Own meaningful customer relationships and deployments end-to-end
Each mandate produces a documented handover that survives any individual departure -your work compounds across customers and across the platform