CUDA Developer

Oriole Networks; City Of London; On-site; GBP 50,000 - 80,000; Full time

Job summary

A leading technology company in the United Kingdom is seeking a talented CUDA Developer to help optimize their AI/ML software stack with advanced network hardware. Candidates should have proficiency in C++ and Python, along with expertise in CUDA programming. The role involves designing custom GPU kernels, maintaining distributed frameworks, and profiling GPU applications. A strong background in high-performance computing and collaboration with hardware teams is essential.

Qualifications

• Proficient in C++ and Python with high-performance computing experience.
• Expertise in CUDA programming and GPU memory hierarchies.
• Hands-on experience debugging GPU kernels.
• Strong understanding of communication libraries and protocols.

Responsibilities

• Design and optimize custom GPU communication kernels.
• Develop and maintain distributed communication frameworks.
• Profile, benchmark, and debug GPU applications.
• Collaborate with hardware and software teams.
• Contribute to system-level architecture decisions.

Skills

C++

Python

CUDA programming

High-performance computing

Debugging GPU kernels

Communication libraries

Distributed deep learning

Tools

Cuda-gdb

Cuda Memcheck

NSight Systems

Docker

Kubernetes

SLURM

Job description

Oriole is seeking talented CUDA Developer 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.