Machine Learning Engineer

ExaCare AI

. Build and maintain workflows and infrastructure supporting the end-to-end ML lifecycle .

Posted 10/3/2026full-timeToronto • CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining ML workflows and infrastructure, optimizing ML systems for performance and cost-efficiency, and collaborating with researchers and practitioners to productionize models. Proven ability to enhance data processing and training pipelines while ensuring system reliability and scalability.

Highest-signal resume keywords

Machine Learning EngineeringMLOpsData Pipeline DevelopmentProduction Workflow ManagementMonitoring and Debugging

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData EngineeringBackend EngineeringModel DeploymentData ProcessingTraining Pipeline DesignSystem OptimizationAutomationPerformance MonitoringScalability Improvement

Soft Skills

CollaborationOwnershipAdaptability

Industry Keywords

ML LifecycleModel HandoffProduction SystemsStartup EnvironmentBest Practices in ML Operations

About the role

Key responsibilities & impact

Build and maintain workflows and infrastructure supporting the end-to-end ML lifecycle
Partner with researchers and ML practitioners to productionize models and enable faster iteration
Design, build, and improve data and training pipelines
Improve data processing, annotation workflows, and ML system efficiency
Deploy and maintain background systems supporting model training and inference
Build tooling and processes for monitoring model performance, system reliability, and operational health
Improve scalability, observability, and reproducibility of ML systems
Optimize ML infrastructure for speed, reliability, and cost-efficiency
Identify bottlenecks and automate or streamline manual ML workflow processes
Establish best practices around ML operations, deployment, and system performance

Requirements

What you’ll need

3+ years of experience in machine learning engineering, MLOps, ML infrastructure, data engineering, or backend/platform engineering in ML environments
Experience supporting ML systems end to end, from model handoff through deployment and monitoring
Strong experience building and owning data pipelines, training pipelines, or other production workflows that support ML
Experience working closely with researchers, data scientists, or ML practitioners to productionize models
Strong software engineering fundamentals and experience building production systems
Experience with monitoring, debugging, and improving production ML or data systems
Track record of improving reliability, scalability, speed, and/or cost efficiency in ML systems
Comfort operating in a fast-moving, startup-style environment with a high degree of ownership

Benefits

Comp & perks

Competitive salary and equity in a high-growth startup
Flexible PTO, take what you need
Medical, dental, and vision coverage
Company off-sites
High-achieving team, including ex-Amazon engineers and alumni of Bain, BCG, Goldman Sachs, and more