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Inference

Genesis

San Francisco, CAJobPosted 4mo agoStill listed 5 days ago

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At a glance

Compensation
No compensation found
Location
San Francisco, CA
Work Authorization
Not specified

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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

RequiredNice to have

Skills

QuantizationMemory ManagementCUDATritonCustom KernelsGPU ClustersDistributed Inference SystemsBatchingSchedulingGraph CompilationMonitoring ToolsDebugging ToolsPythonC++RustGoDistributed SystemsML InfrastructureHigh-Performance ServingInference WorkloadsHardware-Software Interactions

Qualifications

8+ Years Experience

Full job description

What You’ll Do

  • 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

What You’ll Bring

  • 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

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