Senior ML Platform Engineer, Senior ML Platform Engineer

NVIDIA is at the forefront of innovations in Artificial Intelligence, High-Performance Computing, and Visualization. They are seeking a Senior ML Platform Engineer to architect, build, and scale high-performance ML infrastructure using Infrastructure-as-Code practices, while collaborating with researchers to streamline ML experimentation.

Responsibilities:

• 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.)

Requirements:

• BS/MS in Computer Science, Engineering, or equivalent experience
• 5+ 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
• Experience building or operating ML platforms supporting frameworks like PyTorch or TensorFlow at scale
• Deep understanding of distributed training techniques (e.g., data/model parallelism, Horovod, NCCL)
• Expertise with modern CI/CD methodologies and GitOps practices
• Passion for building developer-centric platforms with great UX and strong operational reliability
• Proven ability to contribute code to complex orchestration or automation platforms