Agent-native Database Systems Research/Engineer

Agent-Native Database Systems Research/Engineer

Location: Edinburgh Research Center
Working arrangement: Onsite

Role Overview

The Database Systems Research team develops next-generation data management technologies, spanning database kernels, query processing, storage engines, transaction processing, distributed data systems and emerging AI data infrastructure.

The role is suited to researchers and engineers with strong systems backgrounds who are interested in building high-performance, scalable and intelligent data management systems. Relevant areas include database systems, query optimisation, query execution, storage and indexing, transaction processing, distributed databases, cloud-native systems, hardware-aware database design and AI-native data management.

Key Responsibilities

• Database systems research and development: Design, implement and evaluate components such as query optimisers, execution engines, storage engines, indexing structures, transaction processing systems and distributed data-processing frameworks.
• Query processing and optimisation: Research advanced planning and execution techniques for transactional, analytical, hybrid and AI-driven workloads, including adaptive, vectorised and parallel execution.
• Storage and indexing: Develop efficient storage, indexing, caching, compression and data-layout techniques for structured, semi-structured, multimodal and AI-focused workloads.
• Distributed and cloud-native systems: Explore partitioning, replication, fault tolerance, distributed query execution, resource scheduling and large-scale cloud-native data management.
• AI data infrastructure: Investigate vector search, retrieval-augmented generation, agent memory, semantic data management, knowledge graphs, multimodal data and AI-assisted data processing.
• Performance optimisation: Profile and benchmark database and AI infrastructure systems, identifying bottlenecks across CPU, memory, storage, networking and accelerator resources.
• Research output: Turn research ideas into prototypes, technical reports, patents and publications for leading database and systems venues.
• Collaboration: Work with engineering teams, research groups and academic collaborators, communicating system designs, findings, evaluations and technical trade-offs clearly.

Required Skills and Qualifications

• Master’s or PhD in Computer Science, Computer Engineering or a related discipline.
• Strong background in computer systems, database systems, AI systems, distributed systems, operating systems or related areas.
• Solid understanding of database concepts such as query processing, optimisation, storage engines, indexing, transactions, concurrency control, recovery and distributed data management.
• Practical experience with system design, implementation, evaluation and performance debugging.
• Proficiency in at least one systems programming language such as C, C++, Rust or Go.
• Experience with empirical systems research, including benchmarking, profiling, workload analysis, experiment design and performance interpretation.
• Strong problem-solving and technical communication skills.

Preferred Experience

• Experience contributing to database engines, distributed systems, compilers, operating systems, storage systems or other low-level infrastructure.
• Knowledge of modern database architectures such as distributed databases, HTAP, cloud-native databases, vector databases, graph databases, lakehouse systems or AI-native data platforms.
• Experience with systems such as PostgreSQL, MySQL, DuckDB, Spark, Flink, Velox, ClickHouse, RocksDB or similar technologies.
• Familiarity with hardware-aware systems design involving technologies such as multi-core CPUs, NUMA, RDMA, CXL, NVM, SSDs, GPUs or NPUs.
• Experience with vector search, embedding management, RAG, knowledge graphs, semantic data management or agent-oriented memory.
• Publications at leading database, systems or AI infrastructure venues are desirable but not essential.