Sr​/Principal DevOps Engineer, Machine Learning Infrastructure and Performance

Qualcomm Canada ULC

Engineering Group, Engineering Group >
Machine Learning Engineering

Today, more intelligence is moving to end devices, and mobile is becoming the pervasive AI platform. Building on the smartphone foundation and the scale of mobile, Qualcomm envisions making AI ubiquitous—expanding beyond mobile and powering other end devices, machines, vehicles, and things.

• Maintain local server racks and on-site devices
• Make infrastructure and processes robust, reliable, and efficient
• Identify and remedy items impacting productivity of the development team.

• Operate and improve the Linux self-hosted Git Hub runner fleet: capacity, scheduling, storage, monitoring, recovery, access, and incident response.
• Build reliable Git Hub Actions pipelines and reproducible Docker environments for builds, tests, model benchmarks, artifacts, and releases. Maintain the Git LFS-backed model zoo and its shared caches.
• Own and innovate on the Python task/workflow orchestration infrastructure (Prefect-esque) to make hardware measurements traceable, repeatable, and actionable.
• Steward performance-data ingestion and analysis, including the FastAPI/PostgreSQL-backed service and its clients; improve pytest integration, regression detection, reporting, triage, and release promotion.
• Plan the next scale step: isolate workloads, improve cache and artifact lifecycle, and evaluate elastic/cloud or batch execution where it fits scarce devices and large models.
• Turn project-specific tooling into supported, reusable platform components for other teams.

• Depth in:
  Linux
• Depth in: CI/CD
• Depth in:
  Docker
• Depth in:
  Python

• Linux infrastructure:
  Self-hosted Git Hub Actions runners, systemd, remote file systems (NFS), and resource monitoring.
• CI/CD and containers:
  Git Hub Actions, reusable workflows, Docker, and release automation. Familiarity with Jenkins is a plus.
• Model and artifact management:
  Git LFS, shared caches, and large-model storage (ONNX models).
• Python testing: pytest, pytest-xdist, integration tests, and performance reporting (with run-to-run variation).
• AI performance tooling: ONNX, PyTorch, QAIRT SDK is a plus, Android device execution and profiling is a plus (adb).
• Performance data: REST APIs, FastAPI, and PostgreSQL, or similar libraries/frameworks.
• Future scaling: batch scheduling (ex. IBM Spectrum LSF) or cloud infrastructure for horizontally scaling automation.

• Minimum qualifications:
  
  Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 8+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• Minimum qualifications:
  
  Master's degree in Computer Science, Engineering, Information Systems, or related field and 7+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• Minimum qualifications:
  
  PhD in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

Applicants:
Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disabili or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process.

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