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AI System Researcher

Microtech Global LtdEdinburgh, Scotland🇬🇧United KingdomPosted 26 Aug 2026

Why This Role Stands Out

This hybrid role offers a fantastic opportunity to contribute to cutting-edge AI infrastructure and gain significant research-driven engineering experience alongside senior architects. You'll thrive here if you possess a strong background in distributed systems, operating systems, and machine learning infrastructure, with a passion for performance optimization and innovation. Apply to shape the future of AI at Microtech Global Ltd!

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
Edinburgh, Scotland, United Kingdom
Posted
6 days ago
Load BalancingMachine LearningC++LLMPython

Job Description

We are seeking Systems Research Engineers with a strong interest in computer systems, distributed AI infrastructure, and performance optimization. These roles are ideal for recent PhD graduates or exceptional BSc/MSc engineers looking to build research-driven engineering experience in areas such as operating systems, distributed systems, AI model serving, and machine learning infrastructure. You will work closely with senior architects on real-world projects, helping to prototype and optimize next-generation AI infrastructure.

Required Qualifications and Skills:

Bachelors or Masters degree in Computer Science, Electrical Engineering, or related field.

Strong knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure.

Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization.

Proficiency in C/C++, with additional experience in Python for research prototyping.

Solid grounding in systems research methodology, distributed algorithms, and profiling tools.

Team-oriented mindset with effective technical communication skills.

Desired Qualifications and Experience:

PhD in systems, distributed computing, or large-scale AI infrastructure.

Publications in top-tier systems or ML conferences (NSDI, OSDI, EuroSys, SoCC, MLSys, NeurIPS, ICML, ICLR).

Understanding of load balancing, state management, fault tolerance, and resource scheduling in large-scale AI inference clusters.

Prior experience designing, deploying, and profiling high-performance cloud or AI infrastructure systems.


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