Senior Engineer

Full-stack EngineerSoftware EngineerFull TimeRemoteSeniorTeam 10,001+Since 1993H1B SponsorCompany SiteLinkedIn

Location

California + 1 moreAll locations: California | Washington

Posted

12 days ago

Salary

$184K - $356.5K / year

Seniority

Senior

Postgraduate Degree8 yrs expEnglishCloudKubernetesLinuxPythonPyTorchTensorflowGo

Job Description

Senior Engineer

NVIDIA

• develop innovative solutions that advance AI infrastructure capabilities. • directly influence customer success with breakthrough AI initiatives. • build and deploy custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines constructed on NVIDIA reference architectures. • act as the main technical contact for strategic NCPs, offer remote and on-site support, troubleshoot complex production problems, and guide partner engineering teams on NVIDIA platform guidelines. • deploy and manage AI workloads across DGX Cloud, NCP data centers, and major CSP environments using Kubernetes, containers, and GPU scheduling systems aligned to NCP builds. • profile and tune large-scale training and inference workloads on NCP platforms. • implement observability and SLO/SLA monitoring. • lead detailed efforts to reduce latency, cost, and operational risk. • implement and expand NVIDIA reference architectures on partner platforms, develop integrations with partner control planes and customer environments, and ensure smooth API, data pipeline, and enterprise software connectivity. • build detailed implementation guides, runbooks, and post‑mortem documentation that codify standard methodologies for running NVIDIA AI workloads at scale on NCP platforms.

Job Requirements

  • BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
  • 8+ years of experience in customer facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering, ideally supporting large‑scale cloud or service provider environments.
  • Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant or service-provider platforms.
  • Demonstrated AI/ML experience supporting large‑scale training and inference workloads (e.g., LLMs, generative models, recommendation systems) in production or critically important environments.
  • Solid programming skills in Python/Go, with hands‑on experience using frameworks such as PyTorch or TensorFlow for training and serving.
  • Demonstrated capability to collaborate with customer and partner engineering teams in fast-paced environments, guide intricate technical investigations, and bring issues to root cause and resolution.
  • Excellent communication and technical presentation skills, with the ability to clearly articulate architectures, trade‑offs, and recommendations to both engineering and leadership audiences.

Benefits

  • equity
  • benefits

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