Machine Learning Engineer

Oriole Networks; City Of London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A technology firm is seeking a Machine Learning Engineer to enhance their AI/ML software stack through innovative GPU communication optimizations. The ideal candidate will have extensive experience in C++ and Python, as well as expertise in GPU programming with CUDA. This role entails close collaboration with hardware teams to seamlessly integrate solutions, offering a vibrant environment for impactful projects in high-performance computing.

Qualifications

• Proficient in C++ and Python with a strong track record in HPC or ML.
• Expertise in GPU programming with CUDA and optimization techniques.
• Hands-on experience debugging GPU kernels using specialized tools.

Responsibilities

• Design and optimize custom GPU communication kernels.
• Develop and maintain distributed communication frameworks.
• Collaborate with hardware and software teams for integration.

Skills

C++

Python

GPU programming with CUDA

High-performance computing

Debugging GPU kernels

Understanding of communication libraries

Distributed deep learning

Tools

Cuda-gdb

Cuda Memcheck

NSight Systems

Docker

Linux

Kubernetes

SLURM

Job description

Oriole is seeking talented Machine Learning Engineers to help co‑optimize our AI/ML software stack with cutting‑edge network hardware. You’ll be a key contributor to a high‑impact, agile team focused on integrating middleware communication libraries and modelling the performance of large‑scale AI/ML workloads.

Key Responsibilities

• Design and optimize custom GPU communication kernels to enhance performance and scalability across multi‑node environments
• Develop and maintain distributed communication frameworks for large‑scale deep learning models, ensuring efficient parallelization and optimal resource utilization
• Profile, benchmark, and debug GPU applications to identify and resolve bottlenecks in communication and computation pipelines
• Collaborate closely with hardware and software teams to integrate optimized kernels with Oriole’s next‑generation network hardware and software stack
• Contribute to system‑level architecture decisions for large‑scale GPU clusters, with a focus on communication efficiency, fault tolerance, and novel architectures for advanced optical network infrastructure

Required Skills & Experience

• Proficient in C++ and Python, with a strong track record in high‑performance computing or machine learning projects
• Expertise in GPU programming with CUDA, including deep knowledge of GPU memory hierarchies and kernel optimization
• Hands‑on experience debugging GPU kernels using tools such as Cuda‑gdb, Cuda Memcheck, NSight Systems, PTX, and SASS
• Strong understanding of communication libraries and protocols, including NCCL, NVSHMEM, OpenMPI, UCX, or custom collective communication implementations
• Familiarity with HPC networking protocols/libraries such as RoCE, Infiniband, Libibverbs, and libfabric
• Experience with distributed deep learning/MoE frameworks, including PyTorch Distributed, vLLM, or DeepEP
• Solid understanding of deploying and optimizing large‑scale distributed deep learning workloads in production environments, including Linux, Kubernetes, SLURM, OpenMPI, GPU drivers, Docker, and CI/CD automation