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At a glance
Job overview
The role focuses on building low-latency inference pipelines for on-device deployment and designing distributed inference systems on GPU clusters. Responsibilities include implementing efficient low-level code, optimizing workloads for throughput and latency, and creating monitoring tools to ensure reliability and rapid debugging across both high‑performance and edge environments.
Skills & qualifications
Skills
Qualifications
Full job description
What You’ll Do
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Build low-latency inference pipelines for on-device deployment, enabling real-time next-token and diffusion-based control loops in robotics
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Design and optimize distributed inference systems on GPU clusters, pushing throughput with large-batch serving and efficient resource utilization
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Implement efficient low-level code (CUDA, Triton, custom kernels) and integrate it seamlessly into high-level frameworks
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Optimize workloads for both throughput (batching, scheduling, quantization) and latency (caching, memory management, graph compilation)
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Develop monitoring and debugging tools to guarantee reliability, determinism, and rapid diagnosis of regressions across both stacks
What You’ll Bring
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Deep experience in distributed systems, ML infrastructure, or high-performance serving (8+ years)
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Production-grade expertise in Python, with strong background in systems languages (C++/Rust/Go)
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Low-level performance mastery: CUDA, Triton, kernel optimization, quantization, memory and compute scheduling
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Proven track record scaling inference workloads in both throughput-oriented cluster environments and latency-critical on-device deployments
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System-level mindset with a history of tuning hardware–software interactions for maximum efficiency, throughput, and responsiveness
You've read the whole posting — now see how you match it.