Senior Engineer, NCX

🕒 il y a 1 mois

🌐 Allemagne, Espagne, +1 autres pays – Distant

🇩🇪 Allemagne – Télétravail 🇪🇸 Espagne – Télétravail 🇵🇱 Pologne – Télétravail

💵 zł292 500 - zł650 000 / an

⏰ Temps Plein

🟠 Senior

🧑‍💻 Développeur Full-Stack

👻 Score fantôme 3%

Ce que ça veut dire : la probabilité que cette offre reste ouverte sans recrutement actif.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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NVIDIA

Site WebLinkedInTous les Emplois

10 000+ employés

Fondée en 1993

🏥 Santé

🏭 Fabrication

🤖 Intelligence artificielle

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA est une entreprise technologique de premier plan, spécialisée dans le calcul accéléré et l’intelligence artificielle (IA). NVIDIA est à l’avant‑garde des avancées en GPU (processeurs graphiques), cloud computing, centres de données et réalité virtuelle, avec un accent particulier sur les secteurs du gaming, de l’automobile, de la santé et de la robotique. Ses innovations, telles que NVIDIA Omniverse, transforment les processus numériques traditionnels en permettant des simulations haute fidélité et des tâches de rendu de pointe. Ses applications couvrent de nombreux secteurs, des véhicules autonomes avec NVIDIA DRIVE aux solutions de santé avec NVIDIA Clara, ainsi que des analyses et workflows pilotés par l’IA.

Description

• Lead NVIDIA Cloud Partner Day 2 operational readiness efforts. • Collaborate with NVIDIA Cloud Partners to establish systems, procedures, automation, and operational methods for managing NVIDIA accelerated infrastructure after deployment and activation. • Develop continuous validation methods for GPU, CPU, storage, and network health across large-scale AI clusters. • Establish telemetry, monitoring, alerting, dashboards, and operational signals across compute, GPU, InfiniBand/RoCE networking, storage, Kubernetes, and AI workloads. • Build automated workflows to detect, isolate, drain, repair, validate, and return unhealthy infrastructure to service. • Implement scalable GPU fleet lifecycle strategies, including driver and firmware administration, Kubernetes node maintenance, OS patching, configuration management, upgrades, and configuration drift identification. • Translate NVIDIA NCP requirements and reference architectures into production practices, validation criteria, runbooks, automation, and measurable operational standards. • Develop health signals, SLOs, metrics, acceptance criteria, and ongoing validation mechanisms for infrastructure reliability and service readiness. • Develop reusable tooling, automation, implementation guides, runbooks, operational playbooks, and reference implementations across multiple NCP environments.

🎯 Exigences

• BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience. • 8+ years of experience in infrastructure engineering, Site Reliability Engineering, DevOps, cloud platform engineering, systems engineering, or similar roles supporting large-scale production environments. • Strong experience operating Linux-based distributed systems and cloud infrastructure in production. • Deep understanding of Kubernetes, containers, cluster scheduling, and the operational lifecycle of large multi-node environments. • Strong understanding of production observability, including metrics, logging, alerting, dashboards, health checks, and operations guided by service level agreements. • Experience crafting automation for infrastructure lifecycle management, failure detection, remediation, upgrades, and configuration management. • Strong networking fundamentals and experience troubleshooting complex distributed systems across compute, network, and storage layers. • Programming and automation experience using Python, Go, shell scripting, or similar languages. • Experience managing extensive GPU or accelerated computing infrastructure that supports AI training and inference workloads. • Experience with NVIDIA technologies including DGX/HGX systems, CUDA, NVLink/NVSwitch, NVIDIA networking, InfiniBand, RoCE, GPU Operator, Network Operator, or related NVIDIA infrastructure software. • Proven experience collaborating with NVIDIA Cloud Partners, hyperscale cloud providers, managed AI clouds, or extensive service-provider infrastructure and operating SLOs for large-scale compute infrastructure and using operational data to improve availability, performance, and fleet efficiency. • Extensive knowledge of infrastructure observability tools including Prometheus, Grafana, OpenTelemetry, Alertmanager, and scalable telemetry pipelines. • Knowledge of failure modes related to large distributed AI workloads and the infrastructure features necessary to consistently support extended training and production inference.