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# Senior Applied Research Engineer - Machine Learning | Remote (UK/EU)

- **Company:** SGI, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Profiling, Nvidia CUDA, Computer Programming, Software Debugging, Distributed Computing Environment, Memory Management, Python (Programming Language), Machine Learning, Graphics Processing Unit (GPU), Pytorch, Low Latency
- **Published:** October 3, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pou9cow0hg

## About the Role

* Strong understanding of modern ML architectures and large-scale training pipelines.
 * Hands-on experience running distributed training jobs on multi-GPU systems.
 * Advanced profiling and debugging across CPU, GPU, memory usage, latency, and inter-GPU communication.
 * Strong programming skills in Python.
 * Experience with model scaling and parallelisation strategies, including tensor and pipeline parallelism.

Nice to have

 * Familiarity with NCCL, MPI, and distributed communication primitives.
 * Knowledge of PyTorch and Triton internals.
 * Programming experience with C++ and CUDA.

## Description

You will work on novel technical challenges in large-scale model development and contribute to technology that is changing how major organisations operate. This is an opportunity to join a category-defining company at an early stage and help shape its trajectory., * Profile end-to-end distributed training runs to identify bottlenecks across compute, GPU memory, and inter-GPU communication.

 * Influence architectural decisions to improve efficiency and reliability of large-scale training jobs, including developing Triton/CUDA kernels when needed.
 * Design and implement model scaling, parallelisation, and memory optimisation techniques for training workloads with very large context sizes.
 * Collaborate closely with ML Researchers to diagnose architectural inefficiencies, ensure new research ideas scale efficiently in practice, and share internal knowledge on optimisation.
 * Drive productionisation and serving of models from the research side, including improving inference efficiency via techniques such as quantisation.