Data Infrastructure and AI Engineer - Database Systems / AI Infrastructure / Distributed Systems / Systems Research
- Edinburgh, United Kingdom
Posted yesterday
About the role
Data Infrastructure and AI Engineer - Database Systems / AI Infrastructure / Distributed Systems / Systems Research
We are currently partnered with an advanced technology and research organisation developing next-generation data infrastructure, AI systems, and computing technologies.
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.
Key responsibilities
- 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
Key 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
- Experience developing AI-focused data infrastructure
- 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
Keywords
Data Infrastructure / AI Infrastructure / Data Infrastructure Engineer / AI Engineer / Systems Engineer / Research Engineer / Database Engineer / Database Systems / AI Systems / Distributed Systems / Computer Systems / Operating Systems / Database Internals / Query Processing / Query Optimisation / Storage Engines / Indexing / Transactions / Concurrency Control / Distributed Data Management / Cloud-Native Databases / HTAP / Vector Databases / Graph Databases / Lakehouse / AI-Native Data Platforms / PostgreSQL / MySQL / DuckDB / Spark / Flink / Velox / ClickHouse / RocksDB / TiDB / CockroachDB / LLM / Large Language Models / LLM Quantisation / LLM Inference / On-Device AI / Fine-Tuning / PEFT / Knowledge Distillation / Agentic AI / AI Memory / Vector Search / Embeddings / RAG / Retrieval-Augmented Generation / Knowledge Graphs / Semantic Data / C / C++ / Rust / Go / Python / TensorFlow / GPU / NPU / NUMA / RDMA / CXL / NVM / SSD / Heterogeneous Computing / Performance Optimisation / Benchmarking / Profiling / Systems Research / AI Infrastructure / Data Systems / Distributed Computing
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