Machine Learning Engineer
Toronto, Ontario, Canada
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
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, 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.
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.
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.
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.
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.
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.
Tools in this posting
- Python
- SQL
- AWS
- Azure
- Docker
- Google Cloud (GCP)
- MLflow
- SageMaker
- Lightgbm
- NumPy
- pandas
- NoSQL
- Kubernetes
- scikit-learn
- PyTorch
Source — Tool mentions in context
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).
Benefits in the posting
Full benefits wording- ⚕️Comprehensive health, dental, and vision benefits plan
- 🖥 $1,500 annual learning & home-office stipend
- 🧘🏼 $1,000 annual wellness stipend
- 🍔 Monthly Lunch Stipend
- 🚗 Commuter Benefits
- 🚼 Paid 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
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 annual learning & home-office stipend
🧘🏼 $1,000 annual wellness stipend
🍔 Monthly Lunch Stipend
🚗 Commuter Benefits
🚼 Paid Parental leave
🏝 20 annual PTO days + unlimited sick days
🚀 Quarterly Team Gatherings
☕ In Office Amenities
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Already applied? Track this application
Source & posting history
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
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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- Status in our records
- Active
- First seen by us
- Jun 2, 2026
- Recorded sightings
- 61
- Last seen by us
- Oct 8, 2026
- Employer says posted
- Apr 1, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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