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
Skills Required
- 8+ years experience in distributed systems, ML infrastructure, or high-performance serving
- Production-grade expertise in Python
- Strong background in systems languages (C++, Rust, or Go)
- Low-level performance mastery (CUDA, Triton, kernel optimization, custom kernels)
- Experience with quantization, memory and compute scheduling, and graph compilation
- Proven track record scaling inference for both throughput-oriented clusters and latency-critical on-device deployments
- History of tuning hardware-software interactions for efficiency, throughput, and responsiveness
What We Do
Genesis AI is a global full-stack robotics company developing general-purpose robots with human-level intelligence and capabilities. It aims to build foundational AI models that automate repetitive tasks across applications such as lab work and housekeeping. The company uses a proprietary physics engine to generate synthetic physical-world data, helping train robotics models for diverse real-world environments, and operates across Paris and Silicon Valley.
