Member of Technical Staff, Inference (Bay Area, Remote)

Genesis AI • San Carlos, CA

5d • Remote

About The Position

This role focuses on building low-latency inference pipelines for on-device deployment, enabling real-time next-token and diffusion-based control loops in robotics. The position also involves designing and optimizing distributed inference systems on GPU clusters, pushing throughput with large-batch serving and efficient resource utilization. Additionally, the role requires implementing efficient low-level code (CUDA, Triton, custom kernels) and integrating it seamlessly into high-level frameworks, as well as optimizing workloads for both throughput and latency. Developing monitoring and debugging tools to guarantee reliability, determinism, and rapid diagnosis of regressions across both stacks is also a key aspect of this position.

Requirements

• 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

Responsibilities

• 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 This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed