NVIDIA is at the forefront of innovations in Artificial Intelligence, High-Performance Computing, and Visualization. Our invention—the GPU—functions as the visual cortex of modern computing and is central to groundbreaking applications from generative AI to autonomous vehicles. We are now looking for a Software Engineer to help accelerate the next era of machine learning innovation.
In this role, you will propose and implement engineering solutions to ensure delivery of functional, reliable, secure, and performance-optimal GPU clusters to internal researchers, enable them to focus on training and development by reducing operational disruption and overhead, empower them for self-service continuous improvement on reliability, operational excellence & performance. Your work will empower scientists and engineers to train, fine-tune, and deploy the most advanced ML models on some of the world’s most powerful GPU systems.
What You'll Be Doing:
In this position, you will work with coworkers across the AI Platform organization to understand the pain points of validating, monitoring and operating GPU clusters at scale. Then you will design, develop and maintain engineering solutions to solve those pain points systematically.
You will also research in traditional AIOps and the emerging Agentic AI, and leverage it to further reduce the operation toil.
You will participate in on-call support for systems, platforms built and owned by the team.
What We Need To See:
BS/MS in Computer Science, Engineering, or equivalent experience.
2+ years in software/platform engineering, including 1 year in ML infrastructure or distributed systems.
Experience in software development lifecycle on Linux-based platforms.
Strong coding skills in languages such as Python, C++ or Rust.
Experience with Docker, Kubernetes, GitLab CI, automated deployments.
Experience with AIOps or Agentic AI and apply it successfully in production environment.
Ways To Stand Out From The Crowd:
Proficiency with full-stack development: Relational Data Modeling, DB optimization, REST API Semantics, Javascript, CSS, providing API as a service.
Passion for building developer-centric platforms with great UX and strong operational reliability.
Experience running Slurm or custom scheduling frameworks in production ML environments.
Familiarity with GPU computing, Linux systems internals, and performance tuning at scale.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- BS/MS in Computer Science, Engineering, or equivalent experience.
- 2+ years in software/platform engineering, including 1 year in ML infrastructure or distributed systems.
- Experience in software development lifecycle on Linux-based platforms.
- Strong coding skills in languages such as Python, C++ or Rust.
- Experience with Docker, Kubernetes, GitLab CI, automated deployments.
- Experience with AIOps or Agentic AI and applying it successfully in production environments.
- Proficiency with full-stack development: relational data modeling, DB optimization, REST API semantics, Javascript, CSS (preferred).
- Passion for building developer-centric platforms with great UX and operational reliability (preferred).
- Experience running Slurm or custom scheduling frameworks in production ML environments (preferred).
- Familiarity with GPU computing, Linux systems internals, and performance tuning at scale (preferred).
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
