Data Infrastructure & AI Engineer

European Tech Recruit; City of Edinburgh; On-site; GBP 90,000 - 130,000; Full time

Job summary

European Tech Recruit is seeking a Data Infrastructure and AI Engineer to advance systems at the crossroads of database engineering, AI, and HPC. You will work on challenging research and development problems spanning database internals, distributed data platforms, and memory architectures for intelligent agents.

Based in the United Kingdom, this role emphasizes turning concepts into working systems, rigorous evaluation, and delivering high-performing solutions.

Qualifications

• Master's or PhD in CS/CE/EE/Math or related field.
• Strong foundation in computer systems, databases, AI systems, or distributed systems.
• Proficiency in at least one system language: C, C++, Rust, or Go.
• Experience with deep-learning frameworks such as Python or TensorFlow.
• Experience with empirical research through benchmarking and workload analysis.
• Ability to communicate complex results clearly.

Responsibilities

• Design and implement advanced data and AI infrastructure.
• Investigate database components: query processing, storage, indexing, transactions.
• Explore AI techniques: LLM quantisation, on-device inference, fine-tuning.
• Analyze workloads, benchmark, profile, and run controlled experiments.
• Diagnose performance issues and guide system improvements.
• Collaborate on research projects and communicate findings clearly.

Skills

Systems programming (C, C++, Rust, Go)

Python/TensorFlow experience

Empirical systems research & Benchmark

Independent problem solving

Strong communication & collaboration

Education

Master's degree or PhD in CS/CE/EE/Math

Tools

PostgreSQL/MySQL/DuckDB/Spark/Flink/Velox/ClickHouse/RocksDB/CockroachDB

TensorFlow / AI frameworks

Job description

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.