Inference

Genesis AI; Paris; Hybrid; EUR 90,000 - 150,000; Full time

Job summary

Genesis AI is building cutting-edge distributed ML inference systems for robotics with real-time capabilities. You will design low-latency pipelines for on-device deployment and develop high-performance kernels in CUDA, Triton, and related stacks to push throughput and minimize latency.

The role requires deep expertise in distributed systems and ML infrastructure, with production-grade Python and strong system languages knowledge.

Qualifications

• Deep experience in distributed systems, ML infrastructure, or high-performance serving (8+ years).
• Production-grade Python with strong background in C++, Rust, or Go.
• Low-level performance mastery: CUDA, Triton, kernel optimization, quantization, memory and compute scheduling.
• Proven track record scaling inference workloads in throughput-oriented cluster environments and latency-critical on-device deployments.
• System-level mindset 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 into high-level frameworks
• Optimize workloads for throughput and latency (batching, scheduling, quantization, caching, memory management)
• Develop monitoring and debugging tools to guarantee reliability, determinism, and fast diagnosis of regressions across stacks

Skills

Distributed systems

ML infrastructure

High-performance serving

Python

C++/Rust/Go

CUDA/Triton

Performance optimization

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