ML Systems Engineer: GPU Kernels & Distributed AI

Oriole Networks; Greater London; On-site; GBP 80,000 - 100,000; Full time

Job summary

A tech-focused network company in Greater London is seeking a talented ML Systems/Infrastructure Engineer. The role focuses on optimizing the AI/ML software stack with advanced network hardware. Candidates should be proficient in C++ and Python, with extensive experience in GPU programming and communication protocols. Responsibilities include designing GPU communication kernels, debugging applications, and collaborating on system architecture. Competitive compensation and engaging projects await.

Qualifications

• Strong track record in high-performance computing or machine learning projects.
• Deep knowledge of GPU memory hierarchies and kernel optimization.
• Hands-on experience in deploying and optimizing workloads in production.

Responsibilities

• Design and optimize custom GPU communication kernels.
• Develop distributed communication frameworks for deep learning models.
• Profile and debug GPU applications.
• Integrate optimized kernels with next-generation hardware and software.
• Contribute to system-level architecture decisions for GPU clusters.

Skills

C++

Python

GPU programming with CUDA

Debugging GPU kernels

Communication libraries and protocols

HPC networking protocols/libraries

Distributed deep learning frameworks

Large-scale deployment

Tools

Cuda-gdb

Cuda Memcheck

NSight Systems

Docker

Kubernetes

SLURM

OpenMPI

GPU drivers

Job description

A tech-focused network company in Greater London is seeking a talented ML Systems/Infrastructure Engineer. The role focuses on optimizing the AI/ML software stack with advanced network hardware. Candidates should be proficient in C++ and Python, with extensive experience in GPU programming and communication protocols. Responsibilities include designing GPU communication kernels, debugging applications, and collaborating on system architecture. Competitive compensation and engaging projects await.