Data Infrastructure & AI Engineer

We are seeking a Data Infrastructure and AI Engineer to help advance systems at the crossroads of database engineering, artificial intelligence, and high-performance computing.

In this role, you will work on challenging research and development problems spanning database internals, distributed data platforms, efficient large-language-model execution, and memory architectures for intelligent agents. You will turn concepts into working systems, assess them rigorously, and refine them into reliable, high-performing solutions.

What you'll work on

• Design and implement advanced data and AI infrastructure.
• Investigate database components such as query processing, optimisation, storage engines, indexing, transactions, concurrency control, recovery, and distributed data management.
• Explore efficient AI techniques including LLM quantisation, on-device inference, fine-tuning, knowledge distillation, gradient-free learning, and memory for agentic AI.
• Analyse workloads and conduct benchmarking, profiling, and carefully designed experiments.
• Diagnose performance issues and interpret results to guide system improvements.
• Collaborate on technically complex research and engineering projects, communicating findings clearly to colleagues and stakeholders.
• Build and improve infrastructure for data-intensive and AI-driven applications.
• Develop expertise across query execution, optimisation, storage, indexing, transactions, concurrency, recovery, and distributed data systems.
• Research practical approaches to efficient AI, including model quantisation, edge inference, fine-tuning, distillation, optimisation without gradients, and agent memory.
• Study real-world workloads using benchmarks, profilers, and controlled experiments.
• Identify bottlenecks, investigate system behaviour, and use evidence to shape design decisions.
• Contribute to demanding research and engineering initiatives while presenting technical conclusions clearly to both specialist and non-specialist audiences.

What you'll bring

• A Master's or PhD in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related discipline.
• A strong foundation in areas such as computer systems, databases, AI systems, distributed systems, or operating systems.
• Sound knowledge of core database-system principles.
• Sound knowledge of modern AI-system principles.
• Practical experience in system design, implementation, evaluation, and performance debugging.
• Proficiency in at least one systems programming language, such as C, C++, Rust, or Go.
• Proficiency with at least one deep-learning programming interface or environment, such as Python or TensorFlow.
• Experience conducting empirical systems research through workload analysis, benchmarking, profiling, experiment design, and performance interpretation.
• Strong analytical and problem-solving abilities.
• The confidence to approach ambiguous, open-ended technical problems.
• Clear technical communication skills and a collaborative working style.
• A Master's degree or PhD in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a closely related field.
• Strong knowledge of computer systems, databases, distributed computing, AI infrastructure, operating systems, or related areas.
• A solid grasp of fundamental database architecture and implementation.
• A solid grasp of contemporary AI-system design and deployment.
• Hands-on experience building systems, evaluating implementations, and resolving performance problems.
• Fluency in one or more systems languages, including C, C++, Rust, or Go.
• Experience using a deep-learning language, framework, or interface such as Python or TensorFlow.
• A track record of empirical investigation involving workload characterisation, benchmarking, profiling, experimental methodology, and performance analysis.
• Excellent reasoning and troubleshooting skills.
• Comfort working independently on uncertain or loosely defined technical challenges.
• Strong written and verbal communication, along with an effective team-oriented approach.

Additional experience that would be valuable

• Contributions to databases, data-processing engines, storage platforms, distributed systems, compilers, operating systems, or comparable infrastructure projects.
• Knowledge of distributed, HTAP, cloud-native, vector, graph, lakehouse, or AI-native database architectures.
• Familiarity with the internals of platforms such as PostgreSQL, MySQL, DuckDB, Spark, Flink, Velox, ClickHouse, RocksDB, TiDB, CockroachDB, or similar technologies.
• An understanding of hardware-aware design across multi-core CPUs, NUMA, RDMA, CXL, NVM, SSDs, GPUs, NPUs, or heterogeneous computing environments.
• Experience with vector search, embedding management, retrieval-augmented generation, knowledge graphs, semantic data management, or memory systems for AI agents.
• Publications at leading database, systems, or AI infrastructure venues; these are welcomed but not essential.