Senior Production Engineer - DGX Cloud
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
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We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 1d ago
The date the source published, not the day we noticed it (2026-10-02). Last seen at its source just now.
Is it remote?
US, Remote
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Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay
$184k–287.5k/yr
Read out of the job description by us, not from a structured field. Shown in the posting's own currency and period; we never convert.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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workday employer's own board first seen just now · last seen just now
The listing
NVIDIA DGX Cloud delivers AI services and endpoints for research and production workloads. We are looking for a Senior Production Engineer to build software and automation that make those services reliable, scalable, and safe to operate. The Production Engineering team works on large-scale distributed systems spanning internal and external model endpoints; regional control plane services that orchestrate workloads and route requests; and the GPU/CPU compute infrastructure where inference and agentic workloads run. Our work spans Kubernetes clusters across AWS, Azure, Google Cloud, other partner cloud environments, and on-premises deployments.
What you’ll be doing:
- Build and operate production software, automation, and tooling for control plane services, model deployments, and inference and agentic workloads across DGX Cloud environments.
- Improve the reliability of inference and agentic platforms and services, including NVIDIA Cloud Functions, SGLang- and vLLM-based endpoints, and inference services built with NVIDIA Dynamo, through health validation, safer rollouts, observability, and recovery.
- Improve endpoint availability, inference routing, capacity management, and service health to maintain predictable performance as workloads and demand change.
- Use infrastructure as code and GitOps to deploy, configure, validate, upgrade, and recover services consistently across environments.
- Build workflows for service enablement, model releases, handoff, deprecation, and ongoing operations; replace repeatable manual work with reliable automation.
- Define and instrument SLIs and SLOs for inference and control plane services, including availability and latency, use error budgets to guide reliability improvements, and make production health visible to partner teams.
- Participate in on-call and incident response, troubleshoot failures across routing, model runtimes, software, and infrastructure, and turn recurring issues into automation and durable fixes.
- Collaborate with model, platform, storage, networking, security, and GPU infrastructure teams to design and operate services safely at scale.
What we need to see:
- 8+ years of experience building or operating production services and large-scale distributed systems, including hands-on automation.
- Strong programming skills in Python, Go, or a comparable language, with experience developing tools for production operations.
- Experience with infrastructure as code, configuration management, or GitOps, and with building automation for repeatable service deployments and changes.
- Strong knowledge of Linux, Kubernetes, containers, cloud infrastructure, distributed systems, and networking fundamentals; ability to diagnose failures in production.
- Understanding of SRE principles, including SLIs, SLOs, error budgets, incident response, and reducing operational toil.
- Experience instrumenting services and using metrics, logs, and traces to understand system behavior and improve reliability.
- Clear technical communication and ability to work across engineering teams.
- BS/MS in Computer Science or equivalent experience.
Ways to stand out from the crowd
- Familiarity with technologies such as vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, or NCCL, and with GPU performance analysis.
- Experience building Kubernetes operators, controllers, workload orchestration services, fleet management systems, or self-healing automation.
- Experience with Terraform, Argo CD, CI/CD, policy validation, or safe deployment and rollback systems.
- Experience developing with AI tools and agents.
- Background with production AI inference or agentic workloads, including debugging issues across models, runtimes, Kubernetes, and hardware.
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. We have some of the most forward-thinking and hard-working people on the planet working for us. If you're creative, hard-working and self-motivated, we want to hear from you!
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.