обновлено 1 месяц назад
(Canada) Principal ML System Engineer
176 000 - 195 000$
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Principal ML System Engineer (ML Platforms/MLOps): Defining the technical vision, reference architectures, and infrastructure for scalable machine learning and generative AI platforms with an accent on model training, deployment, serving, observability, and security. Focus on building organization-wide MLOps standards, optimizing large-model inference for performance and cost, and guiding cross-team ML infrastructure initiatives.
Location: Remote in Canada or Mississauga, Canada; remote roles require travel to the Mississauga and/or Salt Lake City offices for onboarding, team events, and semi-annual or annual meetings.
Base salary: US$176,000–US$195,000 annually, plus bonus and benefits.
Company
develops healthcare technology products and provides machine learning and generative AI capabilities across its platform.
What you will do
- Set the technical vision, strategy, and multi-quarter roadmap for the company-wide machine learning platform.
- Define reference architectures and standards for data and ML pipelines covering training, evaluation, deployment, and serving.
- Establish MLOps practices for model CI/CD, registries, feature stores, and experiment tracking, including build-versus-buy decisions.
- Design reliability, observability, performance, monitoring, alerting, and automated remediation practices for production ML systems.
- Define security architecture covering authentication, role-based access control, audit logging, and compliance monitoring.
- Provide technical leadership and mentorship across engineering teams while influencing the organization-wide ML infrastructure roadmap.
Requirements
- Expert-level Python and Java skills with strong software engineering fundamentals.
- Deep experience designing and building ML platforms and MLOps workflows at scale.
- Experience with MLFlow, Kubeflow, Ray, model-serving frameworks, or equivalent tools.
- Extensive experience with cloud platforms such as AWS, Azure, and/or GCP, plus Docker and Kubernetes.
- Demonstrated experience setting technical direction and driving cross-team technical initiatives.
- Availability to work remotely from Canada or within commuting distance of the Mississauga office as applicable.
Nice to have
- Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field.
- Familiarity with Azure Machine Learning, Databricks, serverless environments, and ML frameworks.
- Experience implementing security at scale, including MFA, network security, and compliance monitoring.
- Experience optimizing large-model training and inference, including LLM serving, for performance and cost.
Culture & Benefits
- Full-time employment within the Engineering department.
- Remote workplace arrangement with required travel for specified office events.
- Compensation includes base salary, bonus, and benefits.
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