Principal ML Platform Engineer

at Nvidia
📍 Santa Clara, United States
USD 272,000-425,500 per year
SENIOR
✅ On-site

SCRAPED

Used Tools & Technologies

Not specified

Required Skills & Competences ?

Ansible @ 4 Docker @ 6 Go @ 7 Kubernetes @ 6 Linux @ 3 Terraform @ 4 Python @ 7 Airflow @ 4 CI/CD @ 4 TensorFlow @ 4 KubeFlow @ 4 Rust @ 7 Experimentation @ 4 PyTorch @ 4 GPU @ 4

Details

NVIDIA is at the forefront of innovations in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU functions as the visual cortex of modern computing and is central to applications from generative AI to autonomous vehicles. This role will architect, scale, and optimize high-performance ML infrastructure used across NVIDIA's AI research and product teams to enable training, fine-tuning, and deployment of advanced ML models on large-scale GPU systems.

Responsibilities

  • Design, build, and maintain scalable ML platforms and infrastructure for training and inference on large-scale, distributed GPU clusters.
  • Develop internal tools and automation for ML workflow orchestration, resource scheduling, data access, and reproducibility.
  • Collaborate with ML researchers and applied scientists to optimize performance and streamline end-to-end experimentation.
  • Evolve and operate multi-cloud and hybrid (on-prem + cloud) environments with a focus on high availability and performance for AI workloads.
  • Define and monitor ML-specific infrastructure metrics, such as model efficiency, resource utilization, job success rates, and pipeline latency.
  • Build tooling to support experimentation tracking, reproducibility, model versioning, and artifact management.
  • Participate in on-call support for platform services and infrastructure running critical ML jobs.
  • Drive adoption of modern GPU technologies and ensure smooth integration of next-generation hardware into ML pipelines (examples: GB200, NVLink).

Requirements

  • BS/MS in Computer Science, Engineering, or equivalent experience.
  • 15+ years in software/platform engineering, including 3+ years in ML infrastructure or distributed compute systems.
  • Solid understanding of ML training/inference workflows and lifecycle — from data preprocessing to deployment.
  • Proficiency with containerized workloads and orchestration: Kubernetes, Docker, and workload schedulers.
  • Experience with ML orchestration tools such as Kubeflow, Flyte, Airflow, or Ray.
  • Strong coding skills in languages such as Python, Go, or Rust.
  • Experience running Slurm or custom scheduling frameworks in production ML environments.
  • Familiarity with GPU computing, Linux systems internals, and performance tuning at scale.

Preferred / Ways To Stand Out

  • Experience building or operating ML platforms supporting frameworks like PyTorch, TensorFlow, or JAX at scale.
  • Deep understanding of distributed training techniques (e.g., data/model parallelism, Horovod, NCCL).
  • Expertise with infrastructure-as-code tools (Terraform, Ansible) and modern CI/CD methodologies.
  • Passion for building developer-centric platforms with great UX and strong operational reliability.

Compensation & Benefits

  • Base salary range: 272,000 USD - 425,500 USD (determined based on location, experience, and comparable roles).
  • Eligible for equity and benefits (link to NVIDIA benefits page provided in original posting).

Additional Information

  • Applications for this job will be accepted at least until September 22, 2025.
  • This role involves on-call responsibilities for critical ML platform services.
  • NVIDIA is an equal opportunity employer and fosters a diverse work environment.