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(Canada) Principal ML System Engineer

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Remote from
Canada
Salary
CAD 158k–198k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
29 Oct 2026
Listing views
33
Application actions
1
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AI Summary

The role, at a glance.

This principal-level role owns the technical vision, architecture, and multi-quarter roadmap for PointClickCare’s machine learning platform. The engineer will establish standards for model training, evaluation, deployment, serving, observability, security, and cost-efficient cloud infrastructure. The position requires deep MLOps expertise across Python, Java, Kubernetes, cloud platforms, and tools such as MLflow, Kubeflow, Ray, and model-serving frameworks. It also involves organization-wide influence, build-versus-buy decisions, and mentorship of senior engineers building traditional ML and generative AI capabilities.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a principal technical-authority position spanning platform architecture, production reliability, security, and organizational strategy. Success requires both hands-on depth in scalable ML systems and the ability to align multiple engineering and product teams around shared standards.

Salary analysis

Estimated compensation compared with the broader CA market for similar roles.

Estimated job medianMarket rate
C$178,000
CA market rangeC$155k–C$220k
AI insightThe disclosed yearly base-salary range is CAD 158,000 to CAD 198,000, producing a midpoint of CAD 178,000. A competitive Canadian market range for a principal ML platform/MLOps engineering role is estimated at CAD 155,000 to CAD 220,000 in annual base salary; bonus and benefits may increase total compensation.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you define a reference architecture for an enterprise ML platform supporting both classical ML and LLM workloads?

I would start with common lifecycle stages: governed data access, feature and dataset versioning, experimentation, training, evaluation, model registry, deployment, monitoring, and retirement. The architecture would use reusable interfaces and secure platform primitives while allowing workload-specific compute and serving patterns for batch ML, real-time inference, and LLM applications.

What factors guide your build-versus-buy decisions for MLOps platform components?

I evaluate strategic differentiation, integration complexity, operational burden, security and compliance requirements, vendor maturity, total cost of ownership, and exit risk. I prefer buying commoditized capabilities when they meet governance needs, while building abstractions or services where the organization needs differentiated workflows or deep integration with internal systems.

How would you implement observability and reliability for production model-serving systems?

I would define service-level objectives for latency, availability, throughput, error rates, and model quality, then instrument the platform with centralized metrics, logs, traces, alerting, and runbooks. For ML-specific reliability, I would also monitor data drift, prediction drift, feature freshness, model-version performance, and resource utilization, with safe rollback and automated remediation paths.

Describe how you would secure a centralized ML platform used by many engineering teams.

I would apply least-privilege RBAC, strong authentication including MFA where applicable, workload identities, encrypted data paths, secrets management, network segmentation, and comprehensive audit logging. Governance would include environment separation, approved artifact registries, policy-as-code, vulnerability management, and compliance controls integrated into CI/CD.

How do you gain alignment when setting technical direction across multiple engineering teams?

I begin by understanding team constraints and business outcomes, then publish clear principles, target architectures, decision records, and an incremental roadmap. I create adoption through reference implementations, documentation, enablement, and measurable platform outcomes rather than relying solely on mandates.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

At PointClickCare our mission is simple: to help providers deliver exceptional care. And that starts with our people. As a leading health tech company that’s founder-led and privately held, we empower our employees to push boundaries, innovate, and shape the future of healthcare.

With the largest long-term and post-acute care dataset and a Marketplace of 400+ integrated partners, our platform serves over 30,000 provider organizations, making a real difference in millions of lives. We also reinvest a significant percentage of our revenue back into research and development, ensuring our employees have the resources to innovate and make a lasting impact. Recognized by Forbes as a top private cloud company and honored as one of Canada’s Most Admired Corporate Cultures, we offer flexibility, growth opportunities, and meaningful work.

At PointClickCare, we empower our people to be the architects of a smarter healthcare future; one that is human-first and accelerated by AI to create meaningful and lasting change. Employees harness AI as a catalyst for creativity, productivity, and thoughtful decision-making. By integrating AI tools into our daily workflows, collaboration is enhanced, outcomes are improved, and every team member has the proficiency to maximize their impact. It all starts with our hiring practices where we uncover AI expertise that complements our mission, and we continue to invest in training and development to nurture innovation throughout the employee journey.

Join us in redefining healthcare — so it doesn’t just survive, it thrives. To learn more about PointClickCare, check out Life at PointClickCare and connect with us on Glassdoor and LinkedIn.

**Travel to Office expectations**

For Remote Roles: If this role is remote, there will be in-office events that will require travel to and from the Mississauga and/or Salt Lake City office. These will include, but not limited to, onboarding, team events, semi-annual and annual team meetings.

For Hybrid Roles: If this role is Hybrid, there will be an expectation to reside within commutable distance to the office/location specified in the job listing. This will include, but not limited to, weekly/bi-weekly/monthly events in the office with your specific team. This is a requirement for this role.

Team Summary

This team will serve as the product owner for the machine learning platform capabilities within PointClickCare, working closely with other engineering teams across the organization to identify, build and support traditional machine learning (ML) and hybrid ML/LLMsolutions. This centralized team with deep specialization will closely integrate with key horizontal partners to ensure delivery of safe, scalable, and high-impact AI products.

Job Summary

The Principal AI Machine Learning Platform Engineer will set the technical vision and strategy for the machine learning platform that powers ML and generative AI development across PointClickCare, partnering with Product and Engineering leadership to align that direction with product and business goals. As the technical authority for the ML platform, the Principal Engineer will define the reference architectures, standards, and roadmap for the pipelines, tooling, and infrastructure used for model training, deployment, serving, and monitoring, and will provide technical leadership and mentorship to raise the engineering bar for ML systems company-wide.

Key Responsibilities

  • Partner with product and engineering leadership to translate business and product objectives into a multi-quarter technical strategy and roadmap for the ML platform.
  • Define the reference architectures and standards for scalable data and ML pipelines spanning model training, evaluation, deployment, and serving that engineering teams across the organization build upon.
  • Set the direction and best practices for MLOps across the company — including CI/CD for models, model registry, feature stores, and experiment tracking — and drive build-vs-buy decisions for core platform components.
  • Establish the practices and architecture for reliability, observability, and performance of ML systems in production, including monitoring, alerting, and automated remediation.
  • Establish the security architecture for the ML platform, including authentication, role-based access control, audit logging, and compliance monitoring, and ensure adoption across teams.
  • Define secure, cost-efficient integration and infrastructure patterns for connecting the platform with existing systems, APIs, and data sources at scale.
  • Provide technical leadership and mentorship across engineering teams, guiding senior engineers and influencing the org-wide technical roadmap for ML infrastructure.

Qualifications & Skills

  • Expert level in Python and Java with strong software engineering fundamentals.
  • Deep experience designing and building ML platforms and ML Ops workflows at scale, familiarity with tools such as MLFlow, Kubeflow, Ray, and model-serving frameworks or equivalents.
  • Extensive experience with cloud platforms (AWS, Azure, and/or GCP), containerization, and orchestration (Docker, Kubernetes).
  • Demonstrated track record of setting technical direction and driving org-wide technical initiatives across multiple teams.

Preferred

  • Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field.
  • Sufficient familiarity with Azure Machine Learning components, Databricks processing and serverless environments, and ML Frameworks to support strategic decision making
  • Demonstrable history of leading and sustaining build out of critical cross-team systems
  • Experience implementing security at scale including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring.
  • Experience optimizing large model training and inference (including LLM serving) for performance and cost.

#LI-AJ1

#LI-remote

Compensation

At PointClickCare, base salary is one of the many components that make up our total rewards package. The CAD base salary range for this position is $158,000-$198 000 (not overtime eligible) + bonus + benefits. Compensation is assessed individually and aligned to experience, skills, and market context. The posted range reflects typical expectations for this role.

Additional Information

PointClickCare Benefits & Perks:

Benefits starting from Day 1!

Retirement Plan Matching

Flexible Paid Time Off

Wellness Support Programs and Resources

Parental & Caregiver Leaves

Fertility & Adoption Support

Continuous Development Support Program

Employee Assistance Program

Allyship and Inclusion Communities

Employee Recognition … and more!

It is the policy of PointClickCare to ensure equal employment opportunity without discrimination or harassment on the basis of race, religion, national origin, status, age, sex, sexual orientation, gender identity or expression, marital or domestic/civil partnership status, disability, veteran status, genetic information, or any other basis protected by law. PointClickCare welcomes and encourages applications from people with disabilities. Accommodations are available upon request for candidates taking part in all aspects of the selection process. Please contact recruitment@pointclickcare.com should you require any accommodations. As part of our commitment to a streamlined and equitable hiring experience, PointClickCare uses AI tools to assist with candidate screening and assessment.

When you apply for a position, your information is processed and stored with Lever, in accordance with Lever’s Privacy Policy. We use this information to evaluate your candidacy for the posted position. We also store this information, and may use it in relation to future positions to which you apply, or which we believe may be relevant to you given your background. When we have no ongoing legitimate business need to process your information, we will either delete or anonymize it. If you have any questions about how PointClickCare uses or processes your information, or if you would like to ask to access, correct, or delete your information, please contact PointClickCare’s human resources team: recruitment@pointclickcare.com

PointClickCare is committed to Information Security. By applying to this position, if hired, you commit to following our information security policies and procedures and making every effort to secure confidential and/or sensitive information.

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