Member of Technical Staff focused on building low-latency inference pipelines for robotics. Designing GPU inference systems and optimizing workloads for efficiency and performance.
Responsibilities
Build low-latency inference pipelines for on-device deployment, enabling real-time next-token and diffusion-based control loops in robotics
Design and optimize distributed inference systems on GPU clusters, pushing throughput with large-batch serving and efficient resource utilization
Implement efficient low-level code (CUDA, Triton, custom kernels) and integrate it seamlessly into high-level frameworks
Optimize workloads for both throughput (batching, scheduling, quantization) and latency (caching, memory management, graph compilation)
Develop monitoring and debugging tools to guarantee reliability, determinism, and rapid diagnosis of regressions across both stacks
Requirements
Deep experience in distributed systems, ML infrastructure, or high-performance serving (8+ years)
Production-grade expertise in Python, with strong background in systems languages (C++/Rust/Go)
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