Top Tech Jobs & Startup Jobs in Seattle, WA

Develop architectures for deep learning performance and efficiency, analyze performance trade-offs, and collaborate with teams to enhance AI applications.

Design and build scalable data platforms, orchestration layers, and synthetic data pipelines integrating simulations, AI models, and applications. Implement high-throughput, low-latency data APIs and storage, develop agentic orchestration frameworks, and collaborate with research and engineering to translate domain models into production-grade systems.

Lead performance and scalability engineering for Kubernetes-based accelerated AI runtimes. Analyze end-to-end stack from orchestration to hardware, design upstream Kubernetes changes, improve container startup and encrypted inference performance, build large-scale simulations and automated workload tests, and engage upstream communities and customers to validate and ship scalable AI infrastructure.

Develop and optimize state-of-the-art deep learning and ML techniques for current and next-generation GPUs. Analyze and tune performance, provide GPU-based AI solutions to customers, and collaborate with architecture, research, libraries, tools, and system software teams to influence future hardware and software designs.

Conduct fundamental and applied robotics research across perception, planning, control, and learning. Develop algorithms for manipulation and loco-manipulation, integrate methods into real robotic systems and simulators, publish and engage with academia, transfer research to products, collaborate across teams, and mentor interns.

Lead development and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Design and implement developer tools for GPU-based 3D graphics and neural rendering. Improve existing tooling, collaborate with compiler/driver/research teams, write high-performance C/C++ code, document designs, estimate tasks, and develop test plans with QA.

Design, implement, and optimize GPU-cluster AI/ML infrastructure to boost researcher productivity: identify infrastructure gaps, monitor and tune performance, define efficiency metrics, and collaborate with research, data engineering, and DevOps teams to enable scalable distributed training and inference workflows.

Lead implementation of AI infrastructure solutions on NCP/Neo Cloud and DGX Cloud. Deploy and optimize distributed training/inference, manage Kubernetes/GPU scheduling, profile and tune large-scale workloads, implement reference architectures and integrations, build runbooks and monitoring, and provide remote and on-site technical support to strategic customers.