Senior GPU performance engineer optimizes GPU-based algorithms for self-driving systems, instruments monitoring tools, analyzes hotspots, and collaborates on middleware for efficient compute utilization. Requires 7+ years experience, strong C++/CUDA expertise, and BS in CS.
217k – 307k/yr
Hybrid7+ YOEEmbedded Engineering
About the role
Responsibilities
Build real-time instrumentation for performance monitoring (CPU, GPU, latency, memory) and develop offline benchmarking frameworks, tools, and scripts to evaluate & analyze performance at scale in CI/vehicle, and establish budgets for next-gen architectures.
Analyze performance metrics to identify GPU hotspots and root causes, and propose and co-implement actionable solutions with component teams.
Support teams on bringing serial algorithms to the GPU to maximize compute utilization and improve overall latency.
Work as part of the Core team to design a middleware framework that promotes by default efficient and performant code development by maximizing CPU and GPU.
Qualifications
BS in computer science or related field and 7+ years of experience.
Strong knowledge of CUDA as applied to recent GPU microarchitectures (e.g., Ampere, Blackwell) and experience debugging/optimizing GPU kernels using tools like Nsight.
Strong knowledge of C++ and experience in large code bases, comfortable in Linux development environments.
Experience in development, debugging, and profiling of complex multiprocess systems (e.g., robotic systems, game engines).
Bonus Qualifications
Experience with GPU kernel development in a real-time environment, including PTX-level programming, CPU SIMD instructions (e.g., AVX intrinsics), and custom CUDA layers with frameworks like TensorRT & XLA.
Hands-on work with ML model optimization (post-training quantization, layer pruning, etc) or hand-tuning GPU kernels (in OpenGL, CUDA, RocM or similar).
Proficiency with SQL, DataBricks, Looker, or other business intelligence tools.
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Hybrid7+ YOEEmbedded Engineering
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