5 meilleures offres (computing systems engineer) (à pourvoir)

Senior Site Reliability Engineer, DGX Cloud
NVIDIA —Zürich, ZH
- 100%
- Niveau Master
- AWS
- Réseaux informatiques
il y a 6 jours
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Google —Zürich, ZH
- 100%
- Informatique
- Niveau Doctorat
- Niveau Master
il y a 7 jours
Software Engineer III, Search Quality
Google —Zürich, ZH
- 100%
- Informatique
- Niveau Doctorat
- Niveau Master
il y a 12 jours
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M/A Com Technolgy Solutions —Zürich, ZH
- Niveau Doctorat
- Microsoft Office
- Niveau Master
il y a 7 jours
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M/A Com Technolgy Solutions —Zürich, ZH
- Niveau Licence
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Senior Site Reliability Engineer, DGX Cloud

NVIDIA -
Zürich, ZH

Soumettre votre candidature

Détails de l'emploi

100% | Temps de travail : 90-100%
Il y a 6 jours

Qualifications

TCP
Systèmes d'exploitation
Google Cloud Platform
Niveau Master
AWS
Terraform
Splunk
Réseaux informatiques
Technologies de l'information
Puppet
Linux
Python

Description complète du poste

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA is driving AI and high-performance computing forward. DGX Cloud aims to deliver a fully managed AI platform on major cloud providers, optimizing AI workloads using high-performance NVIDIA infrastructure. Work with NVIDIA's DGX Cloud team as a Senior Site Reliability Engineer to maintain high-performance DGX Cloud clusters for AI researchers and enterprise clients worldwide.

What you’ll be doing:

Build, implement and support operational and reliability aspects of large-scale Kubernetes clusters with focus on performance at scale, real time monitoring, logging and alerting
Define SLOs/SLIs, monitor error budgets, and streamline reporting
Support services before they launch through system creation consulting, developing software tools, platforms and frameworks, capacity management, and launch reviews
Maintain services once they are live by measuring and monitoring availability, latency and overall system health
Operate and optimize GPU workloads across AWS, GCP, Azure, OCI, and private clouds
Scale systems sustainably through mechanisms like automation and evolve systems by pushing for changes that improve reliability and velocity
Lead triage and root-cause analysis of high-severity incidents
Practice balanced incident response and blameless postmortems
Participate in on-call rotation to support production services

What we need to see:

BS in Computer Science or related technical field, or equivalent experience
10+ years of experience operating production services
Expert-level knowledge of Kubernetes administration, containerization, and microservices architecture
Experience with infrastructure automation tools (e.g., Terraform, Ansible, Chef, Puppet)
Proficiency in at least one high-level programming language (e.g., Python, Go)
In-depth knowledge of Linux operating systems, networking fundamentals (TCP/IP), and cloud security standards
Proficient knowledge of SRE principles, encompassing SLOs, SLIs, error budgets, and incident handling
Experience building and operating comprehensive observability stacks (monitoring, logging, tracing) using tools like OpenTelemetry, Prometheus, Grafana, ELK Stack, Lightstep, Splunk, etc.

Ways to stand out from the crowd:

Operating GPU-accelerated clusters with KubeVirt in production
Applying generative-AI techniques to reduce operational toil
Experience with workflow orchestration platforms such as Temporal, Cadence, Airflow, Argo Workflows, or Step Functions
Experience operating and troubleshooting production AI inference workloads across the model-to-GPU stack, including vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, NCCL, and GPU performance analysis

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computing systems engineer emplois

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