ExaCare AI
ExaCare AI
51 – 200 Employees
ConsultingHealthcareLogistics
ExaCare AI develops SaaS software for post-acute care providers, including skilled nursing facilities and home health agencies. Its platform supports referral review, insurance verification, reimbursement workflows, and admissions by summarizing clinical records, identifying clinical and financial risks such as PDPM opportunities, checking coverage eligibility, and providing visibility into occupancy, census trends, and operational performance.

Machine Learning Engineer at ExaCare AI — Hybrid, Toronto

Build the production ML infrastructure, pipelines, and monitoring behind ExaCare AI’s post-acute care platform. Help healthcare teams make safer placement decisions through reliable, scalable machine learning systems.

Description

  • Own workflows and infrastructure across the full machine learning lifecycle
  • Work with researchers and ML practitioners to deploy models and accelerate iteration
  • Create and enhance data and model-training pipelines
  • Streamline data processing, annotation, and overall ML system efficiency
  • Deploy and operate supporting services for model training and inference
  • Develop monitoring tools for model quality, system reliability, and operational performance
  • Increase ML system scalability, observability, and reproducibility
  • Tune ML infrastructure for greater speed, reliability, and cost efficiency
  • Find workflow bottlenecks and automate repetitive ML operations
  • Define and apply best practices for MLOps, deployment, and system performance

Requirements

  • At least three years of experience in ML engineering, MLOps, ML infrastructure, data engineering, or backend and platform engineering for ML environments
  • Experience supporting machine learning systems from model handoff through deployment and monitoring
  • Demonstrated ownership of data pipelines, training pipelines, or other production ML workflows
  • Experience partnering with researchers, data scientists, or ML practitioners to productionize models
  • Strong software engineering fundamentals and experience delivering production systems
  • Experience monitoring, debugging, and improving production ML or data systems
  • Evidence of improving reliability, scalability, speed, or cost efficiency in ML systems
  • Ability to take ownership in a fast-paced, startup-style environment

Benefits

  • Competitive salary and equity at a high-growth startup
  • Flexible paid time off
  • Medical, dental, and vision insurance
  • Company off-site events
  • Collaborative team including former Amazon engineers and alumni of Bain, BCG, Goldman Sachs, and other organizations

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