Inference

Genesis; Paris; On-site; EUR 120,000 - 160,000; Full time

Job summary

Genesis is seeking a senior engineer to build and optimize real-time ML inference pipelines and distributed systems for on-device deployment and GPU clusters. You will write efficient low-level code (CUDA/Triton) and integrate with high-level frameworks, ensuring reliability and rapid diagnosis of regressions across stacks.

The role demands deep expertise in Python and systems languages, plus a track record of scaling inference workloads in both throughput-focused clusters and latency-sensitive

Qualifications

• 8+ years in distributed systems/ML infrastructure/high-performance serving
• Production-grade Python with strong systems languages background (C++/Rust/Go)
• Low-level performance mastery: CUDA, Triton, kernel optimization, quantization, memory/compute scheduling
• Proven track record scaling inference workloads in throughput and latency-critical environments
• System-level mindset tuning hardware–software interactions for efficiency

Responsibilities

• Build low-latency inference pipelines for on-device deployment
• Design and optimize distributed inference systems on GPU clusters
• Implement efficient low-level code (CUDA, Triton, custom kernels)
• Optimize workloads for throughput and latency
• Develop monitoring and debugging tools for reliability and rapid diagnosis

Skills

Distributed systems

ML infrastructure

Python

C++/Rust/Go

CUDA/Triton

Kernel optimization

Quantization

System tuning

Tools

CUDA

Triton

Kernels

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