Principal Performance Engineer (Database & AI Benchmarking)
New
E
EDBData and AI Platform
EU/UKFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- PostgreSQLPythonBashGoDistributed Systems
Requirements
- 8+ years in performance engineering, benchmarking, SRE, or systems engineering (database and/or AI systems)
- Strong understanding of performance concepts and stochastic modeling: latency percentiles, tail behavior, throughput, concurrency, queueing, saturation, and capacity planning
- Strong understanding of database internals (Postgres or equivalent databases)
- Hands-on experience benchmarking databases and/or distributed systems (e.g., Postgres or similar)
- Experience with benchmarking tools and harnesses (examples: pgbench, HammerDB, TPC-like workloads, custom harnesses)
- Proficient in Python and/or Go for automation and data analysis
- Strong scripting skills (Bash)
- Skills in eBPF
- Experience with Linux performance profiling (e.g., perf, flamegraphs, strace, iostat, vmstat)
- Ability to communicate findings clearly and drive performance improvements with engineering teams
Responsibilities
- Set the performance engineering strategy—defining benchmarks, realistic workloads, regression detection and gating, and clear executive-ready reporting
- Partner with Engineering, Solutions Verification, and Product to ensure predictable performance at scale
- Influence product direction by identifying systemic bottlenecks, shaping architectural and roadmap decisions, and leading cross-team initiatives
- Build and maintain repeatable end-to-end benchmarks for database and AI workloads
- Evaluate throughput, latency, concurrency, scalability, and stability across OLTP/OLAP and mixed workloads
- Measure and optimize performance for GPU/CPU inference pipelines, model serving, and data/feature pipelines; assess cost/performance tradeoffs
- Develop harnesses, scripts, dashboards, and reproducible environments to run benchmarks consistently
- Produce clear reports, identify bottlenecks, and recommend fixes
- Partner with database engineers, AI engineers, the field teams and product management to define performance goals and acceptance criteria
- Establish standards for test hygiene, statistical rigor, hardware normalization, and benchmark governance
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