Top Tech Jobs & Startup Jobs in Seattle, WA

Develop and deploy AI infrastructure solutions across NVIDIA cloud, partner, and hyperscale environments. Build distributed training, inference, and MLOps pipelines; manage Kubernetes-based GPU workloads; optimize performance, cost, and reliability; and implement observability and SLO/SLA monitoring. Provide hands-on technical support to strategic customers and partners, troubleshoot production issues, integrate enterprise systems, and create implementation guides, runbooks, and postmortems.

Design, build, and operate Go and Python tooling that validates GPU clusters across Kubernetes and Slurm. Develop certification systems, benchmarks, workload catalogs, provenance and evidence pipelines, platform integrations, and signed auditable results. Investigate performance and correctness issues across distributed AI infrastructure, contribute to open-source projects, and author technical documentation. The role requires deep Linux, container, Kubernetes, HPC, GPU, and distributed-systems expertise, with collaboration across internal teams and cloud partners.

Build and operate reliable full-stack features for NVIDIA’s AI Hub, spanning frontend, backend, and data layers. Own features from design through production support, improve system reliability and observability, collaborate with product and AI research stakeholders, participate in reviews and on-call rotations, and apply modern cloud infrastructure, deployment, testing, and monitoring practices.

Leads complex technical programs for DGX Cloud infrastructure operations, service health, reliability, operational readiness, and platform availability. Coordinates engineering, operations, and reliability teams; manages plans, dependencies, risks, metrics, issue reviews, and maintenance readiness. Translates telemetry and incident trends into prioritized actions and communicates progress, tradeoffs, and recommendations to technical and executive stakeholders.

Lead strategy and execution for NVIDIA’s DGX Cloud platform infrastructure. The role develops roadmaps, execution plans, operating models, dependency maps, and technical tradeoff recommendations across infrastructure, platform software, and engineering teams. The TPM will align stakeholders, influence decisions without direct authority, connect long-term platform goals to delivery, and improve planning, governance, and execution processes.

Leads complex DGX Cloud production engineering programs, coordinating engineering, operations, reliability, and partner teams. Defines program structures, milestones, governance, readiness reviews, risk management, and success criteria. Improves operational rigor, delivery predictability, automation, reliability, and production readiness while communicating status and recommendations to technical and executive stakeholders.

Leads technical engagements with Tier-1 telecommunications customers and partners, mapping workloads to NVIDIA technologies. Designs scalable architectures, integration milestones, and deployment plans; validates solutions; troubleshoots complex issues; enables platform adoption; and communicates customer needs to engineering and product teams. The role requires deep telecom stack expertise, customer-facing technical leadership, and experience with software, hardware, AI inference, networking, containers, Kubernetes, and potentially CUDA/GPU programming.

Lead development and optimization of NVIDIA’s Aerial platform for software-defined 5G/6G RAN systems. Develop baseband software in C, C++, Python, and CUDA; optimize CPU, GPU, and NIC performance; troubleshoot and integrate complex modules; conduct testing, demonstrations, benchmarking, and lab trials; and collaborate with engineering teams, customers, and telecommunications partners on system integration and technical solutions.

Leads engineering teams developing platform software for GPU-accelerated applications using Kubernetes, container runtimes, Kata Containers, and Confidential Containers. Responsibilities include enabling GPUs as Kubernetes resources, integrating software across NVIDIA’s technology stack, managing priorities, and delivering secure, reliable distributed systems. The role requires deep systems software expertise, engineering management experience, and hands-on knowledge of containers, Linux, cloud computing, virtualization, and confidential computing technologies.

Conduct novel research in AI security and cybersecurity, focusing on securing AI models, agents, systems, and infrastructure. Develop tools and processes to identify and remediate vulnerabilities, investigate adversarial machine learning, publish findings, and collaborate across NVIDIA. Present complex technical results to security, machine learning, and business audiences while contributing to secure software development and AI safety.