Software Engineer - AI Research Clusters
NVIDIA
Get more Software Engineering openings
A short daily email when similar roles appear in Santa Clara. No account needed.
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.The average job posting receives 250 applications.
Stand out by tailoring your resume to this specific role. Our AI resume builder highlights the skills and experience that matter most to this employer.
More open roles at NVIDIA
Senior Systems Software Engineer - NV Cloud Functions
Santa Clara
Principal EDA R&D Engineer
Santa Clara
Senior Quantum Systems Engineer - Quantum Computing
Remote
Senior Network Security Architect
Santa Clara
Senior Infrastructure Engineer - AI, Automation, Observability and Monitoring
Santa Clara
Staff Site Reliability Engineer - AI Platform Runtime
Santa Clara
More software engineering roles in Santa Clara
Senior Systems Software Engineer - NV Cloud Functions
NVIDIA
Director of Product Management, Retail
ServiceNow
Staff Software Engineer
ServiceNow
Senior Software Engineer - Traffic and Networking
NVIDIA
Senior Business Systems Analyst - SAP IBP Planning
NVIDIA
Senior Software Engineer, Cosmos Infrastructure and End to End Performance
NVIDIA