Senior Production Engineer – DGX Cloud

🔥 0 minutes ago

🇨🇭 Switzerland – Remote

⏰ Full Time

🟠 Senior

🏭 Production Engineer

👻 Ghost score 11%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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

🎯 Requirements

• 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 Production Engineering 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 practical experience • Familiarity with vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, or NCCL, and 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 • Background with developing with AI tools and agents • Experience with production AI inference or agentic workloads, including debugging issues across models, runtimes, Kubernetes, and hardware