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Day 83
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From 4,296 similar postings that came down. Coming down is not always a hire.
Machine Learning Engineer
- Pay
CAD 100,000–120,000/year — pay source
Salary 100,000 – 120,000 CAD per year At Wave, we value diversity of perspective. Your unique experience enriches our organization. We welcome applicants from all backgrounds. Let’s talk about how you can thrive here!
Read the full posting- Work setting
Remote — work setting source
Work setup in the employer’s posting: remote
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingBuild, train and deploy machine learning models into production
Build scalable ML pipelines and platforms for analytics
Embed ML features into live applications with software developers
Build lineage tracking and data protection into automated systems
Build observability systems to track model health and metrics
From the employer’s posting
Develop & Deploy: Focus on the hands-on building, training, and operational deployment of machine learning models
You will build robust, scalable machine learning pipelines and platforms that support advanced analytics and business intelligence.
smoothly embed ML features into live applications
Direct the integration of lineage tracking and data protection workflows into our automated systems.
Formulate comprehensive observability systems to capture model health and key operational metrics
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Aug 13, 202683 days ago
- Recorded sightings
- 33
- Last seen by us
- Nov 4, 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
Waveapps is hiring
1 open job
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee 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.
- AWS
- Databricks
- MLflow
- Redshift
- SageMaker
- Spark
- Terraform
- Airflow
Where the posting names them
- Technical Depth: Deep understanding of the modern data stack, including data ingestion workflows and experience working with curated data warehouses like Databricks or Redshift. - Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform. - Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.
- Experience: Minimum of 3–5 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments. - Technical Depth: Deep understanding of the modern data stack, including data ingestion workflows and experience working with curated data warehouses like Databricks or Redshift. - Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform.
- Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles. - MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle. - Governance Mindset: Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.
- Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform. - Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles. - MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.
Your next step
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Already applied? Track this application
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
At Wave, we help small businesses to thrive so the heart of our communities beats stronger. We work in an environment buzzing with creative energy and inspiration. No matter where you are or how you get the job done, you have what you need to be successful and connected. The mark of true success at Wave is the ability to be bold, learn quickly and share your knowledge generously.
As a Machine Learning Engineer, you will be a key contributor to the design, development, and deployment of our foundational AI and ML models. You will build robust, scalable machine learning pipelines and platforms that support advanced analytics and business intelligence. This role is perfect for an experienced person who wants to ensure our ML systems are efficient, reliable, and deeply integrated into our organizational goals.
Here's How You Make an Impact:
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Develop & Deploy: Focus on the hands-on building, training, and operational deployment of machine learning models, ensuring they perform reliably within existing production environments.
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Champion Technical Standards: Advocate for top-tier practices across coding, testing, and MLOps processes. Navigate ambiguity autonomously to refine pipelines and elevate ML engineering workflows.
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Optimize & Scale: Construct resilient, cost-efficient ML & AI use cases. Balance sustaining established models with accelerating the rollout of highly scalable, modern systems.
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Partner & Collaborate: Team up with cross-functional stakeholders, including risk specialists, product leads, and software developers, to convert strategic needs into technical specs and smoothly embed ML features into live applications.
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Establish Controls & Governance: Uphold stringent benchmarks for model dependability, fairness, and compliance. Direct the integration of lineage tracking and data protection workflows into our automated systems.
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Track & Evaluate: Formulate comprehensive observability systems to capture model health and key operational metrics, ensuring machine learning investments yield quantifiable organizational value.
You Thrive Here By Possessing the Following:
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Experience: Minimum of 3–5 years of professional experience in machine learning engineering, with a proven track record of deploying models into production environments.
-
Technical Depth: Deep understanding of the modern data stack, including data ingestion workflows and experience working with curated data warehouses like Databricks or Redshift.
-
Cloud Proficiency: At least 3 years of hands-on experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC), Terraform.
-
Orchestration Expert: High proficiency in managing multi-stage workflows using Airflow or similar orchestration systems to automate training and deployment cycles.
-
MLOps Toolkit: Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to support the end-to-end machine learning lifecycle.
-
Governance Mindset: Familiarity with model governance practices (lineage, fairness, and privacy) and experience using data cataloging tools for compliance.
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Communication: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence project direction.
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Industry Context: Experience in FinTech or Financial Risk environments is a significant advantage.
Final compensation is determined based on experience, expertise, and role alignment. Most candidates are hired within the middle of the range, with the upper end reserved for those bringing exceptional depth, impact, and immediate autonomy.
We also offer:
- Bonus Structure
- Employer-paid Benefits Plan
- Health & Wellness Flex Account
- Wellness Days
- Paid Holiday Shutdown
- Wave Days (extra vacation days in the summer)
Visit our site to find out more about life at Wave!
Salary
100,000 – 120,000 CAD per year
At Wave, we value diversity of perspective. Your unique experience enriches our organization. We welcome applicants from all backgrounds. Let’s talk about how you can thrive here!
Wave is committed to providing an inclusive and accessible candidate experience. If you require accommodations during the recruitment process, please let us know by emailing careers@waveapps.com. We will work with you to meet your needs.
We use Google Gemini, a secure AI assistant, during interviews for note-taking purposes only. Notes are kept confidential and are not shared outside the hiring process. This allows our interviewers to stay fully focused on you during the conversation.
This advertised posting is a current vacancy.
Employment type
Full-time
Source details
Source notes
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- Pay
Salary 100,000 – 120,000 CAD per year At Wave, we value diversity of perspective. Your unique experience enriches our organization. We welcome applicants from all backgrounds. Let’s talk about how you can thrive here!
- Location & working pattern
Canada
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.