NVIDIA10.03.26
AI SCORE 9.0

Senior Software Engineer - Deep Learning Compiler Verification (Remote)

$140K–$224K/year

About the Role

We are looking for a Senior Software Engineer - Deep Learning Compiler Verification to join our team at NVIDIA. In this remote role, you will work closely with deep learning compiler engineers to build the infrastructure and automation that powers day-to-day development and releases. This position offers an exciting opportunity to contribute to the next era of computing, where GPUs act as the brain of computers, robots, and self-driving cars.

What You'll Do

  • Design and maintain sophisticated CI/CD systems that run ML workloads at scale across diverse GPU environments.
  • Produce actionable signals for compiler developers, testers, and release engineers to continuously improve stability and turnaround time.
  • Build performance-aware pipelines and workload harnesses that support release confidence and long-term quality of deep learning compiler stacks.
  • Drive CI and infrastructure capabilities that make deep learning compiler development fast, reliable, and scalable.
  • Explore practical uses of AI to enhance CI workflows, such as smarter test selection and automated triage/summarization.

Requirements

  • BS, MS, or PhD in Computer Science, Computer/Electrical Engineering, Mathematics, or related field.
  • 3+ years of professional experience designing and scaling CI/CD, build/release, or developer productivity infrastructure for DL/GPU software environments.
  • Strong software engineering skills in Python with the ability to architect, implement, and debug complex systems end-to-end.
  • Hands-on experience building CI/MLOps platform capabilities, including pipeline orchestration and production-grade observability.
  • Experience with deep learning frameworks such as PyTorch, JAX, or TensorRT.

Nice to Have

  • Experience applying AI/LLMs and agent-based workflows to improve CI and infrastructure.
  • Familiarity with LLVM/MLIR-based toolchains and debugging issues across compilation and runtime execution.

What We Offer

  • Competitive salary range of $140,000 - $224,250 based on experience and location.
  • Equity eligibility and a generous benefits package.
  • A diverse and inclusive work environment that values creativity and autonomy.
  • Opportunities for professional growth and development in a forward-thinking company.
  • Remote work flexibility to maintain a healthy work-life balance.
Language Requirements
EnglishC1
BasicIntermediateAdvancedNative
Why This Job9.0 of 10

This Senior Software Engineer role at NVIDIA offers an exciting opportunity to work remotely on cutting-edge deep learning technology with a competitive salary.

Salary Range
Required
0/1
Optional
0/1
Bonus
0/1

Who Will Succeed Here

Proficient in Python and experienced in using frameworks like PyTorch and JAX for developing and optimizing deep learning models, with a strong understanding of TensorRT for performance tuning.

Self-motivated and disciplined, capable of thriving in a fully remote environment by effectively managing time and delivering on CI/CD pipelines for deep learning compiler projects.

Possesses a deep understanding of Linux-based systems and MLOps practices, enabling the candidate to streamline deployment processes and ensure high reliability in production environments.

Learning Resources

Deep Learning with PyTorch: A 60 Minute Blitzguide

Career Path

Senior Software Engineer - Deep Learning Compiler Verification(Now)Lead Deep Learning Engineer(1-2 years)Principal Engineer / Architect in AI Systems(3-5 years)

Market Overview

Python Market Size 2024
$37B
Annual Growth
11.2%
AI Adoption in Python
65%
Investment in Deep Learning Tools
+150%
Labour Demand for MLOps Engineers
+30%
Avg Salary for Senior Python Engineers
$145K

Skills & Requirements

Required
PythonCI/CDDeep Learning
Growing in Demand
TensorFlowKubernetesData Engineering
Declining
TheanoMapReduce

Domain Trends

Rise of Automated Machine Learning (AutoML)
In 2024, the AutoML market is projected to grow by 25%, as organizations seek to simplify model building and deployment.
Increased Adoption of MLOps Practices
Over 70% of companies are now implementing MLOps frameworks to streamline their ML workflows, significantly improving deployment efficiencies.
Shift towards Edge Computing for AI
By 2025, it is estimated that 30% of AI workloads will be processed at the edge, driven by the demand for real-time processing in IoT devices.

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