Inference

Genesis AI; Greater London; On-site; GBP 110,000 - 150,000; Full time

Job summary

Genesis AI is seeking an expert in distributed systems and ML infrastructure to build and optimize low-latency inference pipelines for on-device robotics. The role focuses on GPU-accelerated architectures and integrating high-performance kernels into user-friendly frameworks.

Candidates should demonstrate production-grade Python skills with a strong background in C++/Rust/Go, and a track record of scaling workloads across both on-device and cluster environments.

Qualifications

• 8+ years in distributed systems, ML infra, or high-performance serving.
• Production-grade Python with strong systems language background (C++, Rust, Go).
• Expertise in low-level optimization: CUDA, Triton, kernels, memory scheduling.

Responsibilities

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

Skills

Distributed systems

Python

C++/Rust/Go

Low-level performance

ML infrastructure

GPU inference

Tools

CUDA

Triton

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