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Montreal Institute for Learning Algorithms

MILA is the machine learning laboratory at the University of Montreal.

Staff Platform Specialist - Sovereign AI Lab

Platform EngineeringConfirmed live in the last 24 hours
Full-timeMontreal, QC, CanadaIn-person

About the Job

Requirements

  • More than 7 years of experience building and operating production infrastructure.
  • Experience leading the design, delivery, and operation of production platforms, including technical reviews, engineering standards, and mentoring.
  • Deep experience deploying, operating, and troubleshooting Linux, containers, Kubernetes, and Docker, including networking and persistent storage.
  • Experience managing infrastructure as code and using tools such as Helm to package and deploy multi-service systems across environments and hardware, with access controls and secrets management.
  • Experience building and operating continuous integration infrastructure and release workflows that test deployment, upgrades, rollback, backup, and restore.
  • Experience managing OpenTelemetry-based infrastructure telemetry from integration to analysis with the Grafana LGTM stack or equivalent.
  • Experience with Ansible and open-source infrastructure-as-code tools, plus strong shell scripting and programming skills in Python or a similar language to automate platforms and build services and tools.
  • Strong written and spoken English and French; ability to explain technical tradeoffs and write clear runbooks and troubleshooting guides enabling partners’ independent stack deployment and operation.

Responsibilities

  • Build and maintain infrastructure-as-code for supported environments and document hardware and infrastructure requirements.
  • Evaluate open-source platform technologies through reproducible tests and benchmarks; record methods, results, limitations, and maintenance risks, and recommend options.
  • Build and maintain versioned deployment packages and shared workflows with the integration team; automate installation, configuration, upgrades, rollback, backup, and restore.
  • Build and run continuous integration infrastructure and reproducible reference environments; automate release tests across supported environments and record results.
  • Build and test platform security controls and secure defaults; test security updates before including them in releases and provide tested playbooks for partners to connect their identity and access systems.
  • Connect infrastructure telemetry to the stack's observability services and package platform dashboards and alerts.
  • Test platform reliability, performance, capacity, and resource use against agreed targets; use results to improve deployment configuration, packages, and automation.
  • Reproduce partner-reported deployment failures in reference environments and release fixes to deployment packages and automation.
  • Maintain and verify runbooks, sizing guides, and troubleshooting instructions so partners can deploy and operate the stack independently.
  • Lead platform design and implementation, set engineering standards, review technical work, and mentor engineers.

Desired Qualifications

  • Experience operating GPUs or other accelerators and AI inference workloads in production.
  • Experience deploying software on infrastructure operated by customers or partners.
  • Experience deploying to air-gapped, regulated, or sovereign environments.
  • Experience maintaining or contributing to open-source infrastructure projects.
  • Experience integrating deployments with external identity services.

About the Company

Montreal Institute for Learning Algorithms

MILA is the machine learning laboratory at the University of Montreal.

Montreal Institute for Learning Algorithms is an artificial intelligence research institute affiliated with Quebec universities. The company conducts machine-learning research, trains students and researchers, publishes scientific work, and collaborates with industry and public organizations. It serves researchers, students, universities, companies, governments, and the broader AI community. Its operating model centers on an academic research community supported by scientific programs, partnerships, computing, and institutional operations. Teams work across machine-learning research, software, education, partnerships, policy, communications, and administration.

Company size
1,001-5,000
Company stage
Grant
Total funding
$5.8M
Headquarters
Montreal, Canada
Founded
1993

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