Data Infrastructure and AI Engineer

Working as part of a database systems research team, this role involves empirical computer science research, system design, and prototype implementation. The position also includes close collaboration with local universities, academic researchers, and expert engineering teams across the UK, Europe, and China.

The office brings together teams working on database systems, programming languages, compilers, knowledge graphs, positioning and navigation, and cloud systems infrastructure. This creates a strong environment for multidisciplinary research at the intersection of data systems, AI, cloud computing, hardware acceleration, and software infrastructure.

This position offers an excellent opportunity to work on impactful research problems in modern data management, where research outcomes can be transferred into real-world database products, on-device AI-powered applications, and large-scale data platforms.

Job Summary

The database team develops next-generation data management systems, with a focus on database kernels, query processing, storage engines, transaction processing, distributed data systems, and emerging AI data infrastructure.

We are looking for researchers and engineers with strong systems backgrounds and a deep interest in building high-performance, scalable, and intelligent data management systems. The role is suitable for candidates with experience in one or more of the following areas: database systems, on-device AI, query optimisation, query execution engines, storage and indexing, transaction processing, concurrency control, distributed databases, cloud-native data systems, hardware-aware database design, AI-native data management, and performance analysis.

The successful candidate will work on both exploratory research and practical system prototyping, contributing to technologies that may influence cloud database platforms, database kernels, and future data infrastructure for AI and intelligent applications.

Key Responsibilities

Database Systems Research and Development

Design, implement, and evaluate core components of next-generation database and data management systems, including query optimisers, execution engines, storage engines, indexing structures, transaction processing, and distributed data processing frameworks.

Query Processing and Optimisation

Research and prototype advanced query planning and execution techniques for transactional, analytical, hybrid, and AI-driven workloads. Explore cost models, adaptive execution, vectorised execution, parallel execution, and workload-aware optimisation.

Storage, Indexing, and Data Layout

Develop efficient storage and indexing mechanisms for structured, semi-structured, multimodal, and AI-oriented data. Investigate data layout, caching, compression, memory hierarchy optimisation, and hardware-aware storage engine design.

Distributed and Cloud-Native Data Management

Explore distributed database architectures, data partitioning, replication, fault tolerance, distributed query execution, resource scheduling, and cloud-native data management techniques for large-scale deployment environments.

AI Data Infrastructure

Investigate database support for emerging AI workloads, including vector search, retrieval-augmented generation, agent memory, semantic data management, knowledge graph integration, multimodal data management, and AI-assisted query and data processing.

On-Device AI

Develop techniques that can run on-device and power the next generation of AI applications. Relevant areas include LLM quantisation, on-device LLM inference, supervised and unsupervised LLM fine-tuning, parameter-efficient fine-tuning, knowledge distillation, gradient-free learning, and memory for agentic AI.

Performance Optimisation and Benchmarking

Conduct rigorous profiling, benchmarking, and empirical performance analysis of database kernels and data processing systems. Identify system bottlenecks and drive optimisation across CPU, memory, storage, network, and accelerator resources.

Research and Publications

Transform research ideas into high-quality prototypes, technical reports, patents, and publications at leading systems, database, and AI venues such as SIGMOD, VLDB, ICDE, CIDR, EDBT, EuroSys, OSDI, SOSP, NSDI, SoCC, NeurIPS, ICML, ICLR, and AAAI.

Cross-Team Collaboration

Work closely with product teams, research teams, and academic collaborators. Communicate research findings, system designs, evaluation results, and technical trade-offs clearly to both research and engineering stakeholders.

This job description is an outline of the tasks, responsibilities, and outcomes required of the role. The jobholder may be asked to carry out other duties as reasonably required by their line manager. The job description and person specification may be reviewed on an ongoing basis in accordance with changing business and research needs.

• Master’s or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related discipline.

• Strong background in computer systems, database systems, AI systems, distributed systems, operating systems, or related areas.

• Solid understanding of database system principles, such as query processing, query optimisation, storage engines, indexing, transaction processing, concurrency control, recovery, or distributed data management.

• Solid understanding of AI system principles, such as LLM quantisation, on-device LLM inference, supervised and unsupervised LLM fine-tuning, parameter-efficient fine-tuning, knowledge distillation, gradient-free learning, and memory for agentic AI.

• Hands-on experience in system design, implementation, evaluation, and performance debugging.

• Proficiency in one or more system-level programming languages, such as C, C++, Rust, or Go.

• Proficiency in one or more deep learning programming interfaces, such as Python or TensorFlow.

• Ability to conduct empirical systems research, including workload analysis, benchmarking, profiling, experiment design, and performance interpretation.

• Strong problem-solving skills and the ability to work on open-ended research and engineering problems.

• Effective technical communication skills and a collaborative mindset.

Desired

• Experience contributing to database systems, data processing engines, storage systems, distributed systems, compilers, operating systems, or other low-level infrastructure projects.

• Knowledge of modern database architectures, such as distributed databases, HTAP systems, cloud-native databases, vector databases, graph databases, lakehouse systems, or AI-native data platforms.

• Experience with database internals in systems such as PostgreSQL, MySQL, DuckDB, Spark, Flink, Velox, ClickHouse, RocksDB, TiDB, CockroachDB, or similar systems.

• Familiarity with hardware-conscious system design, including multi-core CPUs, NUMA, RDMA, CXL, NVM, SSDs, GPUs, NPUs, or heterogeneous computing platforms.

• Experience with AI-related data infrastructure, such as vector search, embedding management, RAG systems, knowledge graphs, semantic data management, or agent-oriented memory systems., * Publications in top-tier database, systems, or AI infrastructure venues are desirable but not essential.