Senior MLOps Engineer - DSX Enablement

NVIDIA is seeking a Senior MLOps Engineer to join our DSX Enablement team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads. We partner with the world's most innovative AI companies and open-source communities to address their most challenging technical problems. What you'll be doing: In this role, you will develop innovative solutions that advance AI infrastructure capabilities, advise infrastructure experts on the demands of ML workloads, help practitioners diagnose and solve full-stack AI and ML system problems, and work on a team with direct responsibility for the success of internal and external customers’ AI and ML initiatives, including LLM performance evaluation and supporting new hardware in open-source frameworks. You will: Build and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners (NCPs), including distributed training, inference optimization, and MLOps pipelines, Act as a primary technical contact for internal and external customers and partners, guiding joint engagements, ensuring the success of initiatives on DGX Cloud, and solving complex problems in production, Work closely with the teams building the infrastructure software and accelerated frameworks that support today’s most compelling AI applications, Profile and tune large-scale training and inference workloads on NCP platforms, leading efforts to reduce latency, cost, and operational risk, and Develop open-source tools and reference architectures to make it easier to build and manage machine learning and AI workloads, pipelines, and systems at scale. What we need to see: BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience. 8+ years of experience in technical roles such as data science, data engineering, or ML engineering, ideally targeting large‑scale production systems. Demonstrated AI/ML experience across multiple phases of the machine learning lifecycle, from exploratory analysis to production systems. Facility with systems topics including Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced networking at datacenter scale. Solid scripting and programming skills in languages like bash and Python and solid systems programming skills in a language like C++, Go, or Rust. Experience using machine learning or deep learning frameworks for training and inference. Excellent communication and technical presentation...

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