Role overview
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 ML Platform Engineer to help accelerate the next era of machine learning innovation.
What you'll work on
- Design, build, and maintain our core ML platform infrastructure as code, primarily using Ansible and Terraform, ensuring reproducibility and scalability across large-scale, distributed GPU clusters.
- Apply SRE principles to diagnose, troubleshoot, and resolve complex system issues across the entire stack, ensuring high availability and performance for critical AI workloads.
- Develop robust internal automation and tooling for ML workflow orchestration, resource scheduling, and platform operations, with a strong focus on software engineering best practices.
- Collaborate with ML researchers and applied scientists to understand infrastructure needs and build solutions that streamline their end-to-end experimentation.
- Evolve and operate our multi-cloud and hybrid (on-prem + cloud) environments, implementing monitoring, alerting, and incident response protocols.
- Participate in on-call rotation to provide support for platform services and infrastructure running critical ML jobs, driving root cause analysis and implementing preventative measures.
- Write high-quality, maintainable code (Python, Go) to contribute to the core orchestration platform and automate manual processes.
- Drive the adoption of modern GPU technologies and ensure smooth integration of next-generation hardware into ML pipelines (e.g., GB200, NVLink, etc.).
What we're looking for
- BS/MS in Computer Science, Engineering, or equivalent experience.
- 8+ years in software/platform engineering or SRE roles, including 3+ years focused on ML infrastructure or distributed compute systems.
- Strong proficiency in Infrastructure-as-Code (IaC) tools, specifically Ansible and Terraform, with a proven track record of building and managing production infrastructure.
- SRE-oriented mindset with extensive experience in diagnosing system-level issues, performance tuning, and ensuring platform reliability.
- Solid understanding of ML workflows and lifecycle—from data preprocessing to deployment.
- Proficiency in operating containerized workloads with Kubernetes and Docker.
- Strong software engineering skills in languages such as Python or Go, with a focus on automation, tooling, and writing production-grade code.
- Experience with Linux systems internals, networking, and performance tuning at scale.
Tags & focus areas
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