Senior MLOps Engineer - DSX Enablement

• 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 skills, with the ability to clearly articulate architectures, trade‑offs, and recommendations to both engineering and leadership audiences.

• A clear record of engineering discipline and execution on interesting projects, whether you're working alone or collaborating on a team.

• 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 and familiarity building machine learning systems in a security-critical environment and distributed training and inference frameworks.

• Familiarity with MLOps practices in a cloud‑native context: containerization, CI/CD pipelines, workflow automation, observability stacks, and GitOps workflows.

• Direct experience drawing on deep systems knowledge to diagnose and fix performance or correctness problems that span multiple layers of the application stack, like hardware, networking, accelerator, hypervisor or OS, compilers or runtimes, application code, and libraries.

NVIDIA offers competitive salaries and a generous benefits package. It is recognized as one of the technology world's most desirable employers. We have some of the most innovative and dedicated people working here. Due to rapid growth, our outstanding teams are expanding quickly. Join us to make a lasting impact on the world!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 292,500 PLN - 507,000 PLN for Level 4, and 375,000 PLN - 650,000 PLN for Level 5.

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.

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.