Senior Engineer

NVIDIA

NCX Senior Engineer developing innovative AI infrastructure solutions for NVIDIA's AI Accelerator team. Collaborating with customers to ensure efficient performance on NVIDIA platforms across various environments.

Posted 7/23/2026full-timeRemote • California, Washington • 🇺🇸 United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing and deploying AI solutions on cloud platforms, with a strong focus on optimizing large-scale training and inference workloads. Proven ability to collaborate with engineering teams and communicate complex technical concepts effectively.

Highest-signal resume keywords

AI/ML ExperienceKubernetesPython/Go ProgrammingLinux SystemsDistributed Computing

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Hard Skills

AI Infrastructure DevelopmentMLOps PipelinesInference OptimizationLarge-Scale Workload ProfilingObservability ImplementationAPI IntegrationData Pipeline ConnectivityNVIDIA Reference ArchitecturesCost Reduction StrategiesOperational Risk Management

Soft Skills

Excellent CommunicationTechnical Presentation SkillsCollaboration with Engineering TeamsProblem-SolvingCustomer Engagement

Tools & Technologies

NCPNeo CloudDGX CloudGPU Scheduling SystemsContainers

Industry Keywords

Cloud EnvironmentsService Provider PlatformsLarge-Scale TrainingGenerative ModelsRecommendation Systems

Tech Stack

Tools & technologies

CloudGoKubernetesLinuxPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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

Comp & perks

equity
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