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ExaCare AI
ExaCare AI
51 – 200 Angajați
Asistență medicalăConsultanțăLogistică
ExaCare AI dezvoltă software SaaS pentru furnizorii de servicii de îngrijire postacută, inclusiv unități de asistență medicală specializată și agenții de îngrijire medicală la domiciliu. Platforma sa sprijină analiza trimiterilor, verificarea asigurării, fluxurile de lucru pentru rambursare și internarea pacienților prin rezumarea dosarelor clinice, identificarea riscurilor clinice și financiare, precum oportunitățile PDPM, verificarea eligibilității pentru acoperire și oferirea unei imagini asupra gradului de ocupare, tendințelor numărului de pacienți și performanței operaționale.
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Locurile de muncă ale companiei

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.

CACanadaHibridNormă întreagăNivel mediuSeniorLimbă Obligatorie: englezăInginer de învățare automată

Descriere

  • 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

Cerințe

  • 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

Beneficii

  • 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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