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 will 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 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.
Ways to stand out from the crowd:
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. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor’s, master’s, or doctoral degree in Computer Science, Computer Engineering, Electrical Engineering, a related technical field, or equivalent experience
- 8+ years of experience in technical roles such as data science, data engineering, or machine learning engineering
- AI/ML experience across multiple lifecycle phases, from exploratory analysis through production systems
- Experience with Linux, batch schedulers, Kubernetes, distributed filesystems, and datacenter-scale networking
- Scripting and programming experience with Bash and Python
- Systems programming experience with C++, Go, or Rust
- Experience using machine learning or deep learning frameworks for training and inference
- Excellent communication and technical presentation skills
- Demonstrated engineering discipline and successful execution on technical projects
- Experience contributing to or working in open-source communities
- Experience with NVIDIA DGX systems, CUDA, NeMo, RAPIDS, Triton, NIM, InfiniBand, NVLink, or RoCE
- Experience building machine learning systems in security-critical environments
- Experience with distributed training and inference frameworks
- Familiarity with cloud-native MLOps, containerization, CI/CD, workflow automation, observability, and GitOps
- Experience diagnosing cross-layer hardware, networking, accelerator, operating system, compiler, runtime, application, or library issues
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
