Senior MLOps Engineer - DSX Enablement

NVIDIA•Santa Clara, CA

9d•$184,000 - $356,500

About The Position

NVIDIA is hiring a Senior MLOps Engineer for its DSX Enablement team. The role partners with strategic customers to implement and improve AI workloads, working with innovative AI companies and open-source communities on challenging technical problems. The engineer develops solutions that advance AI infrastructure, advises infrastructure experts on machine-learning workload demands, and helps practitioners diagnose and solve full-stack AI and ML system problems. The position contributes to the success of internal and external customers’ AI and ML initiatives, including LLM performance evaluation and support for new hardware in open-source frameworks.

Requirements

Bachelor’s, master’s, or Ph.D. degree in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
At least 8 years of experience in technical roles such as data science, data engineering, or ML engineering, ideally focused on large-scale production systems.
Demonstrated AI/ML experience across multiple phases of the machine-learning lifecycle, from exploratory analysis through production systems.
Familiarity with systems topics including Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced networking at datacenter scale.
Scripting and programming skills in languages such as Bash and Python, and systems programming skills in a language such as C++, Go, or Rust.
Experience using machine-learning or deep-learning frameworks for training and inference.
Excellent communication and technical presentation skills, including the ability to explain architectures, trade-offs, and recommendations to engineering and leadership audiences.
A record of engineering discipline and execution on projects, independently or as part of a team.

Nice To Haves

Experience contributing to and working in open-source communities.
Experience with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, RAPIDS, Triton, NIM, and NVIDIA networking technologies such as InfiniBand, NVLink, and RoCE.
Experience building machine-learning systems in a security-critical environment and with distributed training and inference frameworks.
Familiarity with cloud-native MLOps practices, including containerization, CI/CD pipelines, workflow automation, observability stacks, and GitOps workflows.
Direct experience using deep systems knowledge to diagnose and fix performance or correctness problems spanning multiple layers of the application stack, including hardware, networking, accelerators, hypervisors or operating systems, compilers or runtimes, application code, and libraries.

Responsibilities

Build and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners (NCPs), including distributed training, inference optimization, and MLOps pipelines.
Serve as a primary technical contact for internal and external customers and partners, guide joint engagements, help ensure the success of initiatives on DGX Cloud, and solve complex production problems.
Work with teams building infrastructure software and accelerated frameworks for AI applications.
Profile and tune large-scale training and inference workloads on NCP platforms, leading efforts to reduce latency, cost, and operational risk.
Develop open-source tools and reference architectures that make it easier to build and manage machine-learning and AI workloads, pipelines, and systems at scale.