Full-time jobs in Canada

Explore full-time jobs across Canada. Choose a location and read the full job details.

293,980 listings · 42,530 employers Counted Oct 25, 2026. How we count

About these jobs

Employers hiring include Walmart Canada (6,791 listings), Walmart (2,284 listings), Loblaw Companies Limited (2,278 listings).

Common roles: Cook, Project manager, Sales representative.

56,267 of these listings include employer-provided pay. Open a job to see its currency and pay period.

Advertised starting pay
Pay period	Median starting pay	Listings
CAD / hour	24.00	34,223
CAD / year	80,000.00	18,441
USD / year	97,909.20	1,320
USD / hour	20.00	533
CAD / month	4,981.00	464
CAD / week	1,445.00	45
CAD / day	367.50	32
GBP / year	40,000.00	13
USD / month	4,000.00	13
USD / week	1,635.30	11
EUR / year	72,500.00	10

Employer-listed minimums from at least 10 listings per currency and pay period. These are advertised starting amounts, not employee salaries. Jobs without a minimum and unusual amounts are excluded.

Workplace classifications: 2% remote, 16% hybrid, 82% on-site, 0% not specified. Hybrid and on-site classifications may be inferred from descriptions. Check the original ad.

Based on the dated count above. Listings can change. How we count

Full-time jobs in Canada

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Amazon Science

Applied Scientist, Amazon Canada Technology, Amazon Canada

Vancouver, BC, Canada

Full-time

Apply About employer

Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Nov 23, 2026
Experience: 3+ years of building models for business application experience
Posting language: English
Posted: Oct 24, 2026

Original job description

Description

Do you want to solve unique challenges specific to the Canadian market? Are you looking to make an impact on a digital commerce landscape through science?

We are hiring an Applied Scientist to join Amazon Canada Technology and push the science boundaries for Canada: the reasoning LLMs, embedding models, cross-encoder rankers, and multi-objective optimization systems that turn billions of products into the right answer for hundreds of millions of Canadian customers. The role spans the full model lifecycle, from understanding and deciphering where the problem lies, building models, mid-training reasoning models on data such as shopping, returns, reasoning, localization to aligning the models with customers on dimensions that matter for shopping through fulfillment. You will be a pioneer on this team and will own defining the long-term Science strategy and execute on the same by partnering closely with several partner teams such as Core Search, Returns and work with CA Product Managers, Software Engineering and BI/DEs.

Key job responsibilities

Develop personalized multi-modal thinking-LLM techniques that reason about customers, search, queries, ranking and products across both primary and secondary languages.
Train large language models on various data signals and sets: domain-adaptive continued pre-training, reinforcement learning shopping reasoning traces, instruction tuning for natural-language shopping queries.
Analyze Canada (CA) ranking-quality gaps using offline metrics and benchmark CA model staleness and architecture against other marketplace (e.g. US, EU, JP) equivalents.
Formulate falsifiable ranking hypotheses linking a specific feature, architecture, or training change to predicted metric movement.
Run standardized offline evaluation of model candidates; validate feature statistical distributions and coverage in-locale to detect anomalies; own and lead experiments to drive improvements on offline and online experiment benchmarks.
Lead error analysis on failing query segments and segment-level deep dives (head/torso/tail, category, device), surfacing CA-specific failure modes such as bilingual and thin-category gaps.
Own metric-tradeoff analysis and acceptable-regression boundaries, bridging model behavior and CA business priorities.
Partner with teams such as Core Search on hypotheses, model training, and quality gates (MTBR / WLBR / SSAI).
Classify feature-expansion effort tiers (available / pipeline extension / locale training / infeasible) combining locale knowledge with science judgment.

About The Team

Amazon.ca Technology builds the products and services millions of Canadians rely on every day. Our teams tackle challenges unique to the Canadian market while contributing to Amazon initiatives that span the globe - giving you the best of both worlds: meaningful local impact and the scale of global tech. If you want to help shape Canada's digital commerce landscape within a collaborative Canadian work culture, this is the place to do it.

Basic Qualifications

3+ years of building models for business application experience
Experience in CS, CE, ML or related field
Experience programming in Java, C++, Python or related language
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

Experience using Unix/Linux
PhD, or a Master's degree and experience in CS, CE, ML or related field
Experience in understanding performance metrics and developing them to measure progress against key performance indicators

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.

CAN, BC, Vancouver - 149,300.00 - 249,300.00 CAD annually

CAN, ON, Toronto - 149,300.00 - 249,300.00 CAD annually

Apply About employer

More about this job

Responsibilities

Develop and evaluate personalized, multimodal language and ranking models for Canadian shopping, including training models on shopping, reasoning, and localization data. Define the science strategy, analyze Canada-specific quality gaps and trade-offs, lead experiments, and partner with search, product, engineering, and data teams to improve customer outcomes.

Requirements

Candidates need at least three years of experience building models for business applications, a background in computer science, computer engineering, machine learning, or a related field, and programming experience in Java, C++, Python, or a similar language. Experience with algorithms and data structures, optimization, data mining, or distributed and high-performance computing is required; Unix/Linux experience and a master's degree or PhD are preferred.

Skills

Applied Science
Machine Learning
Large Language Models
Reasoning Models
Embedding Models
Cross-Encoder Ranking
Multi-Objective Optimization
Model Training
Reinforcement Learning
Instruction Tuning
Ranking Evaluation
Experimentation
Error Analysis
Python
Java
C++

Benefits

Health Insurance
Dental Insurance
Vision Insurance
Prescription Insurance
Basic Life and Accidental Death and Dismemberment Insurance
Deferred Profit Sharing Plan
Paid Time Off
Sign-On Payments
Restricted Stock Units

Visa sponsorship

Not detected in the job text

Keywords

Applied Science
Machine Learning
Large Language Models
Reasoning Models
Embedding Models
Cross-Encoder Rankers
Multi-Objective Optimization
Multimodal Models
Domain-Adaptive Continued Pre-Training
Reinforcement Learning
Instruction Tuning
Natural-Language Queries
Search Ranking
Offline Evaluation
Online Experiments
Ranking Quality
Metric Trade-Off Analysis
Error Analysis
Feature Validation
Localization
Bilingual Data
Algorithms and Data Structures
Numerical Optimization
Data Mining
Parallel and Distributed Computing
High-Performance Computing
Python
Java
C++
Unix/Linux

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