Top Tech Jobs & Startup Jobs in Seattle, WA

The role involves researching and developing algorithms for deep learning efficiency, collaborating on hardware, running experiments, and publishing findings.

Develop and maintain end-to-end deployment workflows for Warp-based robotics on embedded platforms (e.g., Jetson). Optimize on-device performance, debug across Python/C++/CUDA/ drivers, integrate with ROS 2/Isaac stacks, and build testing and CI/packaging infrastructure for embedded targets.

Lead cross-functional programs to enable DGX Cloud capacity bring-up and operations with CSPs and NVIDIA Cloud Providers. Define requirements for compute, storage, networking and GPUs; manage roadmaps, KPIs, risk mitigation, and engineering deliverables using program management tools to ensure scalable AI infrastructure.

NVIDIA seeks a Senior Systems Software Engineer to develop and maintain foundational software, enhance platform reliability, and collaborate across teams to improve workload execution and diagnostics.

The role involves developing and optimizing AI infrastructure for large-scale training and inference, ensuring system reliability and efficiency through software engineering practices.

The Solutions Architect will lead deployment of NVIDIA data center GPUs, drive partnerships, analyze performance data, and communicate with stakeholders.

Design and implement compiler optimization algorithms for deep learning inference, collaborate with framework and GPU architecture teams, define public APIs, and deliver high-performance AOT and JIT compilation with reduced memory and fast build times.

The role involves defining SoC architecture for low-power platforms, driving PPA trade-off analysis, and collaborating with CPU, GPU, and memory teams. Responsibilities also include engaging with customers and translating their requirements into architectural constraints.

Develop advanced 3D world models by merging map and perception data to enhance autonomous vehicle navigation and safety.

Collaborate with AI/ML researchers to identify and fix infrastructure and application inefficiencies on GPU clusters; build tools and ML-based analyzers; monitor fleet utilization; optimize hardware, software, and distributed training/inference performance; and promote adoption of efficient AI/ML practices.