Systems Research Engineer

European Tech Recruit; City of Edinburgh; On-site; GBP 90,000 - 130,000; Full time

Job summary

European Tech Recruit are partnering with a leading telecommunications and research company based in Edinburgh to hire a Systems Research Engineer. You will join a research centre driving AI infrastructure and large-scale data centre systems, shaping next-generation serving architectures.

This role suits recent PhD graduates eager to build research-driven engineering experience in operating systems, distributed systems, AI model serving and ML infrastructure, collaborating with senior architects

Qualifications

• PhD required in systems, distributed computing, or AI infrastructure.
• Strong knowledge of AI inference serving and ML systems architecture.
• Experience with LLM serving frameworks and KV/cache optimization.

Responsibilities

• Architect and evaluate distributed system components for AI inference workloads.
• Profile and optimize large-scale serving pipelines and memory scheduling.
• Develop scalable, fault-tolerant AI serving across clusters.
• Publish research findings at leading venues and drive internal adoption.
• Collaborate across teams to communicate progress to stakeholders.

Skills

Distributed systems

Operating systems

Machine learning infra

C/C++

Python

Research communication

Education

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

Tools

vLLM

Ray Serve

TensorRT-LLM

TGI

PyTorch Distributed

Job description

European Tech Recruit are working closely with a leading telecommunications & research company, based in Edinburgh, who are looking for a talented Systems Research Engineer to join their team.

In this role you will join a research centre driving new AI Infra & Agentic Serving architectures and helping define the next-generation large-scale data centre and AI infrastructure systems. Positioned at the intersection of advanced systems research and industrial-scale engineering, our client's teams turn innovative system designs into deployable, real-world technologies.

This role is ideal for recent PhD graduates 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.

Responsibilities as Systems Research Engineer:

• Distributed Systems Research & Development: Architect, implement, and evaluate distributed system components for emerging AI and data-centric workloads. Drive modular design and scalability across CPU, GPU, and NPU clusters, building highly efficient serving and scheduling systems.
• Performance Optimization & Profiling: Conduct in-depth profiling and performance tuning of large-scale inference and data pipelines, focusing on KV cache management, heterogeneous memory scheduling, and high-throughput inference serving using frameworks like vLLM, Ray Serve, and modern PyTorch Distributed systems.
• Scalable Model Serving Infrastructure: Develop and evaluate frameworks that enable efficient multi-tenant, low-latency, and fault-tolerant AI serving across distributed environments. Research and prototype new techniques for cache sharing, data locality, and resource orchestration and scheduling within AI clusters.
• Research & Publications: Translate innovative research ideas into publishable contributions at leading venues (e.g., OSDI, NSDI, EuroSys, SoCC, MLSys, NeurIPS, ICML, ICLR) while driving internal adoption of novel methods and architecture.
• Cross-Team Collaboration: Communicate technical insights, research progress, and evaluation outcomes effectively to multidisciplinary stakeholders and global research teams.

Requirements:

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