Time since we first saw it
Day 165
Up longer than most. Similar jobs usually come down between day 8 and day 76.
From 5,517 similar postings that came down. Coming down is not always a hire.
Machine Learning Engineer
Canada · Toronto, Ontario, Canada
Open 5 months, first seen by us 165 days ago- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Machine Learning — team source
Department: Machine Learning
From the employer’s posting
What you’ll work on
Full postingWe’re looking for a Machine Learning Engineer to build and scale high-impact, world-class ML systems.
Design and deploy end-to-end ML pipelines for training, validation, and inference
Implement MLOps practices such as CI/CD, model versioning, monitoring, and retraining
Optimize models through feature engineering, hyperparameter tuning, and scalable inference
Deploy and manage models on AWS, GCP, or Azure using Docker and Kubernetes
Monitor model performance, including bias detection and explainability
From the employer’s posting
We’re looking for a Machine Learning Engineer to build and scale high-impact, world-class ML systems. You’re passionate about deploying AI solutions, optimizing performance, and driving measurable results. Your work will power critical decisions and shape the future of our technology.
Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference.
Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques.
Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes.
Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.
What you’ll bring
All qualificationsCore experience
- Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch.
- Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows.
- Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
- Knowledge of cloud-based ML deployment and infrastructure management.
- Ability to implement real-time and batch inference pipelines efficiently.
Qualification wording
Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch.
Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows.
Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
Knowledge of cloud-based ML deployment and infrastructure management.
Ability to implement real-time and batch inference pipelines efficiently.
Benefits in the posting
Full benefits wording- ⚕️ Comprehensive Health, Dental, And Vision Benefits Plan
- 🖥 $1,500 One-Time Setup + $500/Year Annual Learning & Home-Office Stipend
- 🧘🏼 $1,500 Annual Wellness Stipend
- 🥗 Free Lunch Provided In Office Daily + $250/Month Stipend For Your WFH Days
- 🚗 $150/Month Commuter Benefits
- 🚼 Parental Leave
- 🏝 20 Annual PTO Days + Unlimited Sick Days
From the employer’s posting.
About Bree
Bree is a consumer finance platform building faster, simpler, and more affordable financial services for Canadians who often live paycheck to paycheck.
In the employer’s words · Read in context · Company website ↗
Posting record
Report an errorOpen 5 monthsWe first saw this posting 165 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026165 days ago
- Recorded sightings
- 91
- Last seen by us
- Nov 14, 2026
- Employer says posted
- Apr 1, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Bree is hiring
3 open jobs
- No new data jobs since July
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Azure
- Docker
- GCP
- Kubernetes
- MLflow
- NoSQL
- SageMaker
- LightGBM
- NumPy
- pandas
- PyTorch
- scikit-learn
Where the posting names them
What You'll Need - Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch. - Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
- Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques. - Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation. - Apply machine learning design patterns to build modular, reusable, and production-ready models.
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows. - Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL). - Knowledge of cloud-based ML deployment and infrastructure management.
- Collaborate with data engineers to develop high-performance data pipelines for training and inference. - Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes. - Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.
- Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques. - Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows. - Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Applying through Ashby
- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
About Bree
Bree is a consumer finance platform building faster, simpler, and more affordable financial services for Canadians who often live paycheck to paycheck. We operate in a massive market that’s historically been underserved by traditional financial institutions, and we’re building products that help customers access short-term credit with a transparent, user-first experience.
To date, 800,000+ Canadians have signed up for Bree—and we believe we’re still early. We’re at an exciting intersection of product-market fit, rapid growth, and a clear path to becoming one of the most important fintech companies in Canada.
We were part of Y Combinator (Summer 2021) and raised a $2M seed round shortly after.
About the Role
We’re looking for a Machine Learning Engineer to build and scale high-impact, world-class ML systems. You’re passionate about deploying AI solutions, optimizing performance, and driving measurable results. Your work will power critical decisions and shape the future of our technology.
What You'll Do
Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference.
Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques.
Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation.
Apply machine learning design patterns to build modular, reusable, and production-ready models.
Collaborate with data engineers to develop high-performance data pipelines for training and inference.
Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes.
Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.
What You'll Need
Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch.
Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows.
Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
Knowledge of cloud-based ML deployment and infrastructure management.
Ability to implement real-time and batch inference pipelines efficiently.
Strong analytical and problem-solving skills to translate business needs into scalable ML solutions.
Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy.
Benefits:
💰 Top Of The Market Compensation For Top Performers
⚕️ Comprehensive Health, Dental, And Vision Benefits Plan
🖥 $1,500 One-Time Setup + $500/Year Annual Learning & Home-Office Stipend
🧘🏼 $1,500 Annual Wellness Stipend
🥗 Free Lunch Provided In Office Daily + $250/Month Stipend For Your WFH Days
❄️ Fully Remote In The Winter
🚗 $150/Month Commuter Benefits
🚼 Parental Leave
🏝 20 Annual PTO Days + Unlimited Sick Days
🚀 Annual Company Retreat
☕ In-Office Amenities
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Toronto, Ontario, Canada
🥗 Free Lunch Provided In Office Daily + $250/Month Stipend For Your WFH Days ❄️ Fully Remote In The Winter 🚗 $150/Month Commuter Benefits
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.