Inference at Genesis AI

Bay AreaEngineering & ResearchFull-timePosted 6 months ago

About the role

As published on Genesis AI's job board.

- 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

- 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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Full-stack general-purpose robot company that develops the Genesis physics simulator and robot foundation models.