Senior ML Platform Engineer

Job Description

NVIDIA is seeking a ML Platform Engineer to architect, scale, and optimize high-performance ML infrastructure used across AI research and product teams, empowering the training, fine-tuning, and deployment of advanced ML models.

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, Docker, and workload schedulers
• Experience with ML orchestration tools such as Kubeflow, Flyte, Airflow, or Ray
• Strong coding skills in 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

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 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 the adoption of modern GPU technologies and ensure smooth integration of next-generation hardware into ML pipelines

Benefits

• Employees at NVIDIA are often offered comprehensive, day-one benefits—including medical, dental, and vision coverage with HSA support, life and disability insurance, an Employee Assistance Program, and a 401(k) with auto-enrollment. Many roles also have generous time off and holidays, donation matching (up to $10,000), and a wide menu of extras like FSAs, commuter benefits, legal and identity-theft protection, pet insurance, and wellness discounts. Optional programs can include student-loan and home-purchase support, plus family care resources and expert medical services.