Senior ML Platform Engineer

Details

NVIDIA is at the forefront of innovations in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU is central to groundbreaking 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 empower scientists and engineers to train, fine-tune, and deploy advanced ML models on powerful 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 (e.g., GB200, NVLink).

Requirements

• BS/MS in Computer Science, Engineering, or equivalent experience.
• 7+ 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 in crafting and operating containerized workloads with Kubernetes and Docker, and familiarity with 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.

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 strong UX and operational reliability.

Compensation & Benefits

• Base salary range (determined by location, experience, and internal pay parity):
  • Level 4: 184,000 USD - 287,500 USD
  • Level 5: 224,000 USD - 356,500 USD
• You will also be eligible for equity and benefits.

Additional Information

• Applications accepted at least until August 8, 2025.
• NVIDIA is an equal opportunity employer and is committed to fostering a diverse work environment. NVIDIA does not discriminate 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.