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# AI Runtime Engineer

- **Company:** Modular
- **Location:** Edinburgh, UK
- **Experience:** Experienced
- **Salary:** £82,800.0 - £123,600.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Code Generation, Profiling, Data Transmissions, Microprocessors, Graphics Processing Unit (GPU), High Performance Computing, Distributed Programming, Information Technology
- **Published:** September 18, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5889030180

## About the Role

* 2+ years of experience working on high-performance computing systems.
 * Experience in C++ programming and complex software systems.
 * Experience with CPU or GPU runtime optimizations and performance analysis on CPUs, GPUs, or AI accelerators.
 * Proficiency with one or more profiling tools (CPU or GPU).
 * Creativity and curiosity for solving complex problems, a team-oriented attitude that enables you to work well with others, and alignment with our culture.

 * Experience with ML graph optimizations, parallel / distributed programming, heterogeneous ML computation, and/or code generation.
 * Exposure to MLIR, LLVM, and/or the Mojo programming language.
 * Advanced degree in Computer Science or a related area is a plus.

## Description

A core part of this offering is a platform that enables customers to achieve state-of-the-art performance across model families and frameworks. As an AI Runtime Engineer, you will own a runtime that operates on various CPU, GPU, and accelerator hardware platforms, optimizing performance for diverse customer AI models. LOCATION: Candidates based in the United Kingdom are welcome to apply. This role will be based in our Edinburgh office (minimum 3 days per week on-site) with relocation assistance provided for eligible candidates. All new hires complete onboarding in-person.

 * Design and develop runtime and cross-stack optimizations to improve CPU, GPU, and accelerator efficiency, addressing issues such as CPU overhead, caching, and data locality across multiple devices.
 * Work with vendor-specific networking libraries to unlock high performance data transfer for multiple topologies.
 * Collaborate with the compiler, kernels, serving, and models teams to design core technologies that achieve state-of-the-art end-to-end performance on various CPU and GPU hardware.
 * Collaborate with the customer success team and engage with customers to understand their performance requirements and use cases.
 * Collaborate with tooling and infrastructure teams to design systems for automated performance analysis and benchmarking.