Inference

Genesis AI; Paris; On-site; EUR 120,000 - 180,000; Full time

Job summary

Genesis AI in Paris is seeking an experienced ML infrastructure engineer to build low-latency inference pipelines for real-time robotics, enabling on-device and cluster deployments with high throughput.

You will optimize distributed systems (CUDA, Triton) and develop monitoring tools to ensure reliability, determinism, and rapid diagnosis of regressions across stacks.

Qualifications

• 8+ years in distributed ML infrastructure or high-performance serving.
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Responsibilities

• Build low-latency inference pipelines for on-device deployment in robotics.
• Design and optimize distributed inference systems on GPU clusters for high throughput.
• Implement efficient low-level code (CUDA, Triton, kernels) and integrate into high-level frameworks.
• Optimize workloads for throughput and latency (batching, scheduling, caching, memory).
• Develop monitoring and debugging tools to guarantee reliability and rapid diagnosis of regressions across stacks.

Skills

Distributed systems

ML infrastructure

High-performance serving

Python

C++

Rust

Go

CUDA

Triton

Kernel optimization

Quantization

Memory scheduling

Tools

GPU clusters

Profiling tools

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