Top Tech Jobs & Startup Jobs in Seattle, WA

Develop AI compiler solutions, optimize GPU programming, build training pipelines, collaborate on integrating AI policies, and conduct rigorous performance benchmarking.

Lead technical vision and strategy for next-generation graphics developer tools. Design and prototype tooling, drive cross-team initiatives, mentor engineers, engage external developers and partners, and deliver production-ready solutions that improve graphics development, debugging, profiling, and optimization workflows.

Lead technical pre-sales and solution integration for NVIDIA networking in AI/ML and HPC environments. Work with cloud providers, hyperscalers, and customers on POCs, performance debugging, workshops, and product strategy recommendations to drive adoption of NVIDIA networking hardware and fabrics.

Design and build a scalable failure-attribution framework for heterogeneous CPU/GPU clusters: collect high-fidelity telemetry, implement low-overhead tracing, automate root-cause classification (hardware, software, environment), and enable proactive resiliency measures like migration or checkpointing.

Partner with architecture and design teams to plan and execute functional and performance verification for GPU memory subsystem IP. Develop testbenches, BFMs, monitors and regressions, close coverage, enable FPGA/software testing, and support post-silicon verification and debug to sign off RTL.

Design and implement CUDA/C++ libraries to accelerate Apache Spark DataFrame and I/O operations, collaborate on distributed multi-node GPU solutions, contribute to open-source projects (RAPIDS, CCCL, UCX), and drive testing, CI, and cross-platform optimization for enterprise deployments.

Develop high-fidelity performance models for AI systems, prioritize features for software and hardware roadmaps, and evaluate the performance of GenAI workflows.

The Senior Software Engineer will optimize AI workloads using machine learning techniques for large scale GPU and CPU clusters, focusing on resource allocation, performance analysis, and collaboration on hardware/software teams.

Lead architecture, design, and development of hardware-accelerated AI networking software (BlueField DPU, ConnectX). Drive deep technical engagements with hyperscalers for design-in, bring-up, performance tuning, failure analysis, and production hardening. Partner cross-functionally to turn customer needs into product features, tooling, and roadmap items while improving performance, reliability, and debuggability across stacks.

Design and build AI-native, high-performance CI/build/test/validation infrastructure for large Tegra C/C++ codebases. Optimize build graphs, caching, incremental builds, remote execution, device-lab testing, and integrate reasoning agents for autonomous triage, remediation, and self-healing workflows. Package reusable platform capabilities, drive adoption, and measure developer feedback loop improvements.