Data Infrastructure and AI Engineer - Database Systems / AI Infrastructure /Distributed System[...]

European Tech Recruit

Edinburgh, UK

10 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.collegerecruiter.com

Tech stack

Artificial Intelligence Big Data Compilers Encodings Databases Computer Engineering Concurrency Controls Data Infrastructure Microprocessors Distributed Data Store Distributed Systems Systems Theories

+15 more

Graph Database Multiprocessing Query Optimization Remote Direct Memory Access Search Technologies AI Infrastructure Transaction Processing (Computing) Data Processing Graphics Processing Unit (GPU) Large Language Models Indexer Information Technology Data Management Machine Learning Operations Virtual Agents

Job description

As part of their continued investment in advanced systems research, they are looking to hire a Data Infrastructure and AI Engineer to work at the intersection of database systems, distributed infrastructure, machine learning systems, and low-level computing., * Design, implement, and evaluate next-generation data infrastructure and AI systems

Research and develop innovative approaches to database systems, distributed data management, and AI infrastructure
Investigate database architecture, query processing, query optimisation, storage engines, indexing, transaction processing, concurrency control, recovery, and distributed data management
Develop and optimise systems supporting modern AI workloads, including large language models and agentic AI applications
Research techniques including LLM quantisation, on-device inference, supervised and unsupervised fine-tuning, parameter-efficient fine-tuning, knowledge distillation, and gradient-free learning
Investigate memory architectures and data management techniques for agentic AI systems
Develop system prototypes and conduct rigorous empirical evaluations
Analyse workloads and identify system-level performance bottlenecks
Design and execute benchmarks, experiments, and performance evaluations
Profile complex systems and diagnose performance, scalability, and efficiency issues

Requirements

Master’s or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related technical discipline
Contributions to database systems, data processing engines, storage systems, distributed systems, compilers, operating systems, or other low-level infrastructure projects
Experience with hardware-conscious system design and optimisation
Familiarity with multi-core CPUs, NUMA, RDMA, CXL, NVM, SSDs, GPUs, NPUs, or heterogeneous computing architectures
Knowledge of vector search and embedding management
Experience with Retrieval-Augmented Generation (RAG) systems
Knowledge of knowledge graphs and semantic data management
Experience developing memory systems or infrastructure for agentic AI
Experience optimising systems for AI workloads and large-scale data processing
Research publications in leading database, systems, or AI infrastructure conferences and journals
Experience translating academic research into production-quality systems or prototypes