Top Tech Jobs & Startup Jobs in Seattle, WA

Design, deploy, maintain, tune, and secure enterprise Linux environments supporting high-performance EDA compute farms. Lead infrastructure-as-code automation, configuration management, continuous deployment, and tooling development using Python, Bash, or Go. Manage LDAP/SSSD naming services, NFS and Autofs storage, virtualization, container infrastructure, registries, networking, and infrastructure telemetry. Support highly available, scalable systems and potentially integrate cloud platforms, CI/CD pipelines, EDA toolchains, and HPC clusters.

Develop and productize autonomous-driving perception software. Responsibilities include full SDLC execution, writing and testing C++ and Python code, integrating perception components, creating verification plans, deploying deep-learning models to embedded platforms, debugging systems, and evaluating performance with real and synthetic data.

Design and develop physical layouts for silicon interposers used in NVIDIA products. Collaborate with package technical leads, signal integrity and technology engineers, and design teams to deliver package solutions. Conduct feasibility studies, floorplanning, custom layout, design-rule and netlist verification, and layout trade-off analysis. Resolve manufacturing, electrical, cost, and scheduling issues while using Cadence tools and verification flows such as DRC and LVS.

Owns the segment sales strategy and revenue growth for NVIDIA DOCA and BlueField infrastructure software. Builds pipelines, forecasts, customer and partner plans, and commercialization paths across networking, cybersecurity, storage, cloud infrastructure, and data services. Develops lighthouse customers and ecosystem partnerships, shapes complex pricing and licensing opportunities, leads executive and technical discovery, and enables sellers and partners to communicate measurable customer value. Requires extensive enterprise infrastructure software sales, business development, technical sales, and partner go-to-market experience.

Lead the technical direction and development of NVIDIA Dynamo, an open-source, production-scale AI inference platform. Drive initiatives in resilience, performance, scalability, and inference infrastructure while collaborating across storage, networking, hardware, and performance teams. Represent Dynamo with customers and the AI community, guide technical strategy, and promote emerging AI/ML infrastructure technologies. The role requires deep systems software, production inference, and senior technical leadership experience.

Build and lead development of NVIDIA’s open-source NeMo Core Platform for executing, evaluating, deploying, and optimizing agentic AI systems across local, Docker, Kubernetes, Slurm, on-premises, and air-gapped environments. Design platform APIs, plugins, SDKs, job orchestration, authentication, RBAC, observability, testing, and reliability features. Provide technical leadership through architecture and code reviews, mentoring, and ownership of complex cross-component problems.

Design and implement high-performance, scalable storage systems and client libraries for AI workloads. Build data loading, checkpointing, caching, POSIX-style and object-store integrations, observability and telemetry, and validate performance with platform, SRE, and AI teams using modern engineering practices.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Develop and optimize GPU-accelerated numerical linear algebra libraries for AI and high-performance computing. Design kernel generation, JIT compilation, matrix operations, and operator fusions; lead projects involving multiple engineers and teams; provide technical mentorship; collaborate with product management and customers on requirements and roadmaps; and improve performance and maintainability through architectural enhancements.

Develop and support NVIDIA CUDA Math Libraries across emerging and specialized platforms. Responsibilities include platform integration, functional qualification, defect triage, bug fixing, performance benchmarking, regression resolution, and building CI/CD qualification pipelines. The role requires high-performance numerical software development using C++ and Python, parallel computing, numerical methods, and software architecture, with preferred experience in CUDA, GPU computing, math libraries, cross-platform development, and embedded or safety-critical systems.