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
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Inference employer: Genesis AI
Genesis is an exceptional employer for those looking to make a significant impact in the AI and robotics field. With a competitive salary, meaningful equity, and a hybrid work model, employees enjoy a dynamic work culture that fosters innovation and collaboration. The opportunity to shape the recruiting function from the ground up, alongside industry leaders, ensures that team members can grow professionally while tackling some of the most challenging problems in technology today.
