Top Tech Jobs & Startup Jobs in Seattle, WA

Manage and lead a software engineering team building NVIDIA DGX Cloud Kubernetes platforms. Responsibilities include gathering requirements, guiding engineers, developing cloud-based solutions, presenting roadmaps and demos, participating in open-source communities, and partnering across NVIDIA organizations. The role requires expertise in distributed systems, systems programming, Kubernetes, Linux internals, networking, storage, DevOps tooling, container orchestration, APIs, and identity and access management.

Develop platform software using container runtimes and Kubernetes to enable GPU-accelerated applications. Build GPU resource support for container orchestrators, integrate software across NVIDIA’s technology stack, and contribute to cloud-native systems. The role requires systems programming, Linux internals, distributed systems, cloud computing, and performance, security, and reliability expertise. Responsibilities include solving complex problems, managing multiple priorities, and collaborating across engineering teams.

Develop Rust-to-GPU compiler pipelines, custom intermediate representations, optimizers, and ahead-of-time, just-in-time, and link-time compilation tooling for NVIDIA GPUs. Design Rust-based safety abstractions across GPU execution boundaries and expose low-level CUDA hardware features. Responsibilities include compiler architecture, GPU code generation, debugging, profiling, benchmarking, and independently driving complex engineering initiatives from research through production.

Design and implement GPU compiler components, optimize compiler performance, solve graphics and parallel-computing compilation problems, collaborate with GPU driver and architecture teams, contribute to LLVM and DXC open-source projects, and help define SPIR-V extensions and standards.

Leads NVIDIA’s AI Cluster Runtime product, setting direction for a validated, reproducible GPU Kubernetes software stack. Expands support across accelerators, clouds, and AI workloads; oversees version-locked recipes, supply-chain provenance, and reliable deployment. Represents the project to customers, partners, and the open-source community while driving community adoption. Requires deep Kubernetes GPU infrastructure expertise, GitOps configuration delivery experience, and a strong open-source contribution record.

Lead and grow a technical team responsible for verifying AI compilers for next-generation workloads. Define formal verification requirements, drive AI-assisted and compiler-aware verification (fuzzing, differential testing, symbolic reasoning, formal methods), and partner with compiler, CUDA, runtime, and ML framework teams to build scalable verification infrastructure and improve production confidence.

Design and develop RTOS and hypervisor features, bring up and optimize software stacks on NVIDIA Tegra SoCs, and debug system-level issues across hardware, firmware, RTOS, and virtualization layers. The role develops high-integrity automotive software using safety and security practices, while leading cross-team technical initiatives and coordinating execution. Candidates need strong C/C++ systems programming, embedded software expertise, operating-system knowledge, hardware-assisted virtualization experience, and at least eight years of experience.

Lead distributed engineering and data teams developing autonomous-vehicle data curation systems and algorithms. Architect in-car data strategies supporting AI training, simulation, and continuous performance improvement. Develop algorithms to mine and classify valuable driving scenarios, establish data quality and metadata standards, and coordinate alignment across research, engineering, and product leadership. The role requires extensive industry experience, technical leadership, autonomous vehicle or robotics expertise, and familiarity with multimodal AI systems.

Leads the architecture and development of enterprise infrastructure automation and configuration management platforms across compute, storage, networking, data centers, cloud, and hybrid environments. Defines long-term technical strategy, builds secure and scalable integrations, applies AI to configuration intelligence and remediation, and remains hands-on with software development. Mentors senior engineers, establishes engineering standards, drives enterprise adoption, and partners with leadership on technical roadmaps and business outcomes.

Lead technical program management for NVIDIA GPU software, coordinating chipset and product roadmaps with software capacity. Manage cross-functional dependencies, prioritization, planning gaps, hardware and release-planning partnerships, process improvements, and execution metrics. The role requires delivering software products while balancing short-term critical timelines with long-term improvements to planning, processes, and team capabilities.