Principal Performance Engineer (Database & AI Benchmarking)
New
Fully remote role within the UK/EU regionFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 8+ years of experience in performance engineering, systems engineering, SRE, or benchmarking roles, ideally spanning database and/or AI systems.
- Required Skills
- PostgreSQLPythonBashGoLinux
Requirements
- 8+ years of experience in performance engineering, systems engineering, SRE, or benchmarking roles, ideally spanning database and/or AI systems.
- Strong expertise in performance concepts such as latency distributions, tail behavior, concurrency, saturation, and capacity planning.
- Deep understanding of database systems (preferably PostgreSQL or similar) and experience benchmarking distributed or large-scale systems.
- Hands-on experience with benchmarking frameworks such as pgbench, HammerDB, TPC-style workloads, or custom performance harnesses.
- Strong programming and automation skills in Python and/or Go, with solid Bash scripting and data analysis capability.
- Experience with Linux performance profiling tools such as perf, flamegraphs, strace, iostat, or vmstat.
- Ability to clearly communicate complex technical findings and influence engineering decisions across teams.
- Strong plus: experience with vector databases, PostgreSQL tuning, AI inference pipelines, GPU profiling, RAG workflows, or storage/network performance optimization.
Responsibilities
- Define and lead the performance engineering strategy across database and AI workloads, including benchmarking frameworks, regression detection, and performance reporting standards.
- Design and maintain repeatable, end-to-end benchmarks for OLTP, OLAP, and mixed database workloads as well as AI inference and pipeline systems.
- Develop and optimize benchmarking harnesses, automation scripts, and dashboards to ensure consistent and reproducible performance evaluation.
- Analyze system performance across throughput, latency, concurrency, scalability, and stability, identifying bottlenecks and recommending optimizations.
- Collaborate with engineering, product, and field teams to define performance targets, acceptance criteria, and architectural improvements.
- Establish performance best practices, including test rigor, workload modeling, hardware normalization, and governance standards.
- Provide executive-ready insights and reports translating complex performance data into clear technical and business recommendations.
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