Senior Software Engineer – Deep Learning Compiler CI Infrastructure

NVIDIA

Senior Software Engineer building and enhancing CI/CD infrastructure supporting deep learning compilers at NVIDIA. Collaborating with engineers to improve developer efficiency and reliability of systems.

Posted 4/29/2026full-timeSanta Clara • California, New York, Oregon, Washington • 🇺🇸 United StatesSenior💰 $140,000 - $224,250 per yearWebsite

Tech Stack

Tools & technologies

Distributed SystemsJenkinsPython

About the role

Key responsibilities & impact

Build, maintain, and improve CI infrastructure that supports development, verification, and release of NVIDIA’s deep learning compiler stacks across GPU and accelerator environments
Improve CI reliability and signal quality by reducing flakes, improving reproducibility, strengthening diagnostics, and making correctness and performance failures easier to understand and act on
Apply automation, AI, and agent-based workflows to reduce manual CI operations, speed up failure triage, and improve developer efficiency
Build reusable and self-service CI platforms that support multiple products, projects, model suites, hardware targets, and software configurations while partnering closely with compiler, infrastructure, and release teams

Requirements

What you’ll need

BS, MS, or PhD (or equivalent experience) in Computer Science, Computer/Electrical Engineering, Mathematics, or a related field
5+ years of experience designing, scaling, and operating CI/CD, build/release, or developer infrastructure for complex software systems
Proven experience building CI platforms end-to-end using systems such as GitLab CI, GitHub Actions, Jenkins, or similar tools, including pipeline orchestration, compute/runner management, artifact and package systems, and observability, with strong emphasis on reliability, reproducibility, and debuggability
Strong software engineering skills (Python required), with the ability to design, implement, and debug distributed systems end-to-end
Proven track record of designing, building, and deploying AI/LLM-based systems in real engineering workflows, demonstrating skill in evaluating trade-offs, failure modes, maintainability, and measurable impact on developer productivity, signal quality, or operational efficiency

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development
Bonuses
Equity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

CI/CDbuild/release infrastructurepipeline orchestrationcompute managementartifact systemspackage systemsobservabilityPythondistributed systemsAI/LLM-based systems

Soft Skills

problem-solvingcollaborationcommunicationdebuggingdesign skillsanalytical skillsefficiency improvementreliability focustrade-off evaluationmaintainability assessment