Top Tech Jobs & Startup Jobs in Seattle, WA

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 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 lane-level localization, perception-map association, and lane selection systems using navigation maps. Develop efficient, safe C++ algorithms and productize software to automotive quality and safety standards for deployment in consumer autonomous vehicles. Work with large-scale real and synthetic datasets and collaborate across teams.

Design and implement Rust-to-GPU compiler backends, IR frameworks, and JIT/AOT toolchains. Lower Rust AST/MIR to MLIR/LLVM/PTX, build GPU-specific optimizers, enforce safe parallel abstractions across host/device boundaries, and expose ergonomic device-side primitives. Drive projects from research to production and support profiling, debugging, and benchmarking workflows.

Develop and implement compiler optimization algorithms and code generation for deep learning workloads (XLA/OpenXLA) targeting NVIDIA GPUs. Design graph partitioning and tensor sharding for distributed training/inference, performance tune models, work with ML frameworks (JAX) and hardware teams, and contribute to compiler/tooling (MLIR/LLVM/Triton).

Design and implement GPU cluster scheduling and orchestration features, optimize resource usage and performance for large multi-node AI workloads, develop automation and batch workload management, perform performance analysis and root-cause investigations, support users on scheduler issues, and build the GPU-accelerated computing ecosystem.

Design and implement an LLVM-based backend JIT compiler for NVIDIA GPUs. Develop compiler passes and optimizations, focusing on high performance and low compile time. Collaborate with hardware, compiler, and application teams to resolve issues and influence future GPU architectures.

Lead design, integration, and delivery of SONiC networking features for NVIDIA products. Develop high-quality C++/Python code in an open-source, continuous-deployment environment, collaborate with internal and external teams, manage chassis/networking features, and engage with the SONiC community to drive next-generation switch solutions.

Design and develop software solutions for data center servers, focusing on Linux kernel modifications, device drivers, and system optimizations while directly collaborating with Cloud Service Providers.

Design and build real-time, physics-informed AI models and synthetic data pipelines for quantum calibration and decoding. Develop surrogate models and GPU-accelerated implementations, apply reinforcement and online learning for calibration policies, co-design low-latency calibration-decoding systems, and collaborate across engineering and research to enable fault-tolerant quantum hardware.