Member of Technical Staff, Inference

Role Description

• 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.

Qualifications

• 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).
• Low-level performance mastery: CUDA, Triton, kernel optimization, quantization, memory and compute scheduling.
• Proven track record scaling inference workloads in both throughput-oriented cluster environments and latency-critical on-device deployments.
• System-level mindset with a history of tuning hardware–software interactions for maximum efficiency, throughput, and responsiveness.