ExaCare AI
ExaCare AI
ExaCare AI malakali parvarish muassasalari va uyda tibbiy yordam ko‘rsatuvchi agentliklarni o‘z ichiga olgan, o‘tkir davolashdan keyingi tibbiy xizmat ko‘rsatuvchilar uchun SaaS dasturiy ta’minotini ishlab chiqadi. Uning platformasi klinik yozuvlarni umumlashtirish, PDPM imkoniyatlari kabi klinik va moliyaviy xatarlarni aniqlash, sug‘urta qamrovi muvofiqligini tekshirish hamda bandlik darajasi, bemorlar soni tendensiyalari va operatsion samaradorlik haqida ko‘rinish berish orqali yo‘llanmalarni ko‘rib chiqish, sug‘urtani tasdiqlash, to‘lovlarni qoplash jarayonlari va bemorlarni qabul qilishni qo‘llab-quvvatlaydi.

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.

Tavsif

  • 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

Talablar

  • 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

Imtiyozlar

  • 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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