Senior Software Engineer, Capacity Management - DGX Cloud

AI in this role

Design and build distributed backend systems and capacity-management infrastructure for large-scale GPU AI cloud workloads.

kubernetespythongojavacloud-infrastructure

distributed-systemsbackend-servicescapacity-managementdata-pipelines

NVIDIA DGX Cloud provides the infrastructure and software platform that enables enterprises to build, train, and deploy AI at scale. As demand for accelerated computing grows, effective capacity management is critical to delivering reliable customer experiences while maximizing the utilization of constrained GPU infrastructure.

We are looking for a Senior Software Engineer to design and build the systems that connect customer demand, infrastructure supply, reservations, allocation, and utilization across DGX Cloud environments. You will work with engineering, product, operations, finance, and business teams to transform complex capacity data and operational processes into scalable software and automated decision-making.

What You’ll Be Doing:

Design and build distributed services and data pipelines for capacity planning, allocation, reservations, and utilization.
Develop a unified model of available, committed, and forecasted GPU capacity across cloud providers, regions, clusters, and products.
Automate capacity-management workflows currently dependent on manual analysis and coordination.
Build APIs, tools, and integrations that enable other DGX Cloud systems and teams to make capacity-aware decisions.
Improve forecasting, scenario planning, and operational visibility by combining demand signals with infrastructure supply data.
Establish monitoring, data-quality controls, and service-level indicators for capacity systems.
Lead technical design reviews, establish engineering standards, and mentor other engineers.
Diagnose complex production issues and improve the reliability, performance, and scalability of capacity-management services.

What We Need to See:

BS or equivalent experience in Computer Science, Computer Engineering, or a related technical field.
5+ years of software engineering experience building production systems.
Strong programming experience in languages such as Python, Go, Java, or similar.
Experience designing distributed systems, backend services, APIs, and data-processing pipelines.
Experience working with cloud infrastructure, Kubernetes, compute platforms, or large-scale resource-management systems.
Strong understanding of data modeling, system integration, observability, and production operations.
Ability to turn ambiguous business and operational requirements into clear technical designs.
Strong communication skills and experience working across engineering and non-engineering organizations.

Ways to Stand Out From the Crowd:

Experience with GPU infrastructure, AI/ML platforms, schedulers, cluster management, or accelerated computing.
Experience building capacity planning, inventory, supply-and-demand, quota, reservation, or resource-allocation systems.
Familiarity with optimization, forecasting, simulation, or operations-research techniques.
Experience managing infrastructure across multiple cloud providers or geographically distributed environments and serving as a technical lead for cross-functional, business-critical initiatives.
Demonstrated success improving infrastructure utilization while maintaining reliability and customer commitments.

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is looking for phenomenal people like you to help us accelerate the next wave of artificial intelligence. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people in the world working for us.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 140,000 USD - 224,250 USD for Level 3, and 168,000 USD - 270,250 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 15, 2026.

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.

How we rate this

Senior Software Engineer, Capacity Management - DGX Cloud at NVIDIA rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Distributed SystemsBackend ServicesCapacity ManagementData PipelinesKubernetesPythonGoJava

Questions you could be asked

Tell me about a project where distributed systems was part of your work. What did you do?
Tell me about a project where backend services was part of your work. What did you do?
Tell me about a project where capacity management was part of your work. What did you do?
Tell me about a project where data pipelines was part of your work. What did you do?
Walk me through how you've used Kubernetes in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Distributed Systems, Backend Services, Capacity Management, Data Pipelines, and Kubernetes. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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