NVIDIA is hiring a Senior ML Platform Engineer - Lepton

NVIDIA is looking for a Senior ML Platform Engineer to join the Lepton team. You will architect, build, and scale high-performance machine learning infrastructure using Infrastructure-as-Code practices. Your work will create reliable, automated platforms that empower scientists and engineers to train and deploy advanced ML models on powerful GPU systems.

What You'll Do

• Design, build, and maintain core ML platform infrastructure as code using Ansible and Terraform for reproducibility and scalability across large-scale, distributed GPU clusters.
• Apply SRE principles to diagnose, troubleshoot, and resolve complex system issues across the entire stack to ensure 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 focus on software engineering best practices.
• Collaborate with ML researchers and applied scientists to understand infrastructure needs and build solutions that streamline end-to-end experimentation.
• Evolve and operate 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).

What We're Looking For

• BS/MS in Computer Science, Engineering, or equivalent experience.
• 8+ years in software/platform engineering or SRE roles.
• 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.

Nice to Have

• 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.

Technical Stack

• Infrastructure: Ansible, Terraform, Kubernetes, Docker
• Languages: Python, Go
• ML Frameworks: PyTorch, TensorFlow
• Distributed Training: Horovod, NCCL

Benefits & Compensation

• Compensation: $184,000 USD - $287,500 USD for Level 4, and $224,000 USD - $356,500 USD for Level 5.
• Equity
• Benefits

NVIDIA is committed to fostering a diverse 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.