Senior MLOps Engineer - DSX Enablement

Sourced from mikeishiring.com

NVIDIA's DSX Enablement team is hiring a Senior MLOps Engineer to help strategic customers deploy and optimize large-scale AI workloads on NeoCloud and NVIDIA Cloud Partner platforms. The role spans distributed training, inference optimization, MLOps pipelines, and full-stack performance tuning of production ML systems. You will also build open-source tools and reference architectures and serve as a primary technical contact for customers and partners.

• Build and deploy custom AI solutions on NeoCloud and NVIDIA Cloud Partner platforms, including distributed training, inference optimization, and MLOps pipelines
• Act as primary technical contact for internal and external customers and partners on DGX Cloud engagements
• Profile and tune large-scale training and inference workloads to reduce latency, cost, and operational risk
• Collaborate with infrastructure software and accelerated framework teams
• Develop open-source tools and reference architectures for managing ML and AI workloads at scale

• BS, MS, or PhD in Computer Science, Computer/Electrical Engineering, or related field, or equivalent experience
• 8+ years in technical roles such as data science, data engineering, or ML engineering, targeting large-scale production systems
• Demonstrated AI/ML experience across the machine learning lifecycle, from exploratory analysis to production
• Systems knowledge: Linux, batch schedulers, Kubernetes, distributed filesystems, and datacenter-scale networking
• Scripting in bash and Python plus systems programming in C++, Go, or Rust
• Experience with machine learning or deep learning frameworks for training and inference
• Strong communication and technical presentation skills

• Open-source community contributions
• NVIDIA ecosystem experience: DGX, CUDA, NeMo, RAPIDS, Triton, NIM, InfiniBand, NVLink, RoCE
• Building ML systems in security-critical environments and distributed training/inference frameworks
• Cloud-native MLOps: containerization, CI/CD, workflow automation, observability, GitOps
• Deep systems debugging across hardware, networking, hypervisor, OS, compilers, and runtimes