Principal Performance Engineer (Database & AI Benchmarking)

Location: Remote India

Department: Product Development

We are hiring a Principal Performance Engineer to make performance a core company competency across our database and AI workloads. You will set the performance engineering strategy—defining benchmarks, realistic workloads, regression detection and gating, and clear executive-ready reporting—and partner with Engineering, Solutions Verification, and Product to ensure we deliver predictable performance at scale. You will influence product direction by identifying systemic bottlenecks, shaping architectural and roadmap decisions, and leading cross-team initiatives 

What you will be doing:

• Benchmark design & execution: Build and maintain repeatable end-to-end benchmarks for database and AI workloads
• Database performance engineering: Evaluate throughput, latency, concurrency, scalability, and stability across OLTP/OLAP and mixed workloads.
• AI performance benchmarking: Measure and optimize performance for GPU/CPU inference pipelines, model serving, and data/feature pipelines; assess cost/performance tradeoffs.
• Tooling & automation: Develop harnesses, scripts, dashboards, and reproducible environments to run benchmarks consistently.
• Analysis & reporting: Produce clear reports, identify bottlenecks, and recommend fixes.
• Cross-functional collaboration: Partner with database engineers, AI engineers, the field teams and product management to define performance goals and acceptance criteria.
• Performance best practices: Establish standards for test hygiene, statistical rigor, hardware normalization, and benchmark governance.

What you will bring:

• 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.

What will give you an edge:

• Experience with active benchmarking techniques.
• Experience with vector databases / vector search benchmarking (recall/latency tradeoffs, indexing, tuning).
• PostgreSQL performance tuning (autovacuum, WAL, replication, connection scaling).
• RAG pipelines: ingestion + embeddings + vector retrieval + reranking + generation.
• Familiarity with AI stack components: model serving, GPU profiling
• Background in storage and networking performance (IO patterns, filesystem tuning, kernel tuning).