Senior MLOps Engineer - DSX Enablement

NVIDIA is hiring a Senior MLOps Engineer to join their DSX Enablement team, working with strategic customers and open-source communities to enhance AI workloads and infrastructure.

• BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or related field, or equivalent experience.
• 8+ years of experience in technical roles such as data science, data engineering, or ML engineering targeting large-scale production systems.
• Demonstrated AI/ML experience across multiple phases of the machine learning lifecycle.
• Experience with Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced datacenter-scale networking.
• Proficient scripting and programming skills in bash, Python, and systems programming in C++, Go, or Rust.
• Experienced with machine learning or deep learning frameworks for training and inference.
• Strong communication and technical presentation skills.
• Proven engineering discipline and execution in solo and team projects.

Nice to have:

• Experience contributing to open-source communities.
• Familiarity with the NVIDIA ecosystem including DGX, CUDA, NeMo, RAPIDS, Triton, NIM, and NVIDIA networking technologies like InfiniBand, NVLink, and RoCE.
• Experience building ML systems in security-critical environments and distributed training/inference frameworks.
• Knowledge of MLOps practices in cloud-native contexts including containerization, CI/CD pipelines, workflow automation, observability stacks, and GitOps.
• Deep systems knowledge to diagnose and fix multi-layer application stack performance and correctness issues.

• Build and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners including distributed training, inference optimization, and MLOps pipelines.
• Act as the primary technical contact for customers and partners, guiding joint engagements and solving complex production problems.
• Collaborate with teams building infrastructure software and accelerated AI frameworks.
• Profile and tune large-scale training and inference workloads to reduce latency, cost, and operational risk.
• Develop open-source tools and reference architectures for managing ML and AI workloads and pipelines at scale.