Principal Engineer - Enterprise Scale & AI Performance

Requirements Experience: 12+ years scaling multi‑tenant SaaS with a reputation for removing major bottlenecks and proving impact with data

• Technical expertise: Performance engineering, capacity planning, sharding/partitioning, caching/back‑pressure, multi‑region readiness, and high‑volume migrations; you turn hotspots into robust patterns
• AI tools & automation: You apply AI to scale work—profiling assistance, workload modeling, synthetic traffic generation, anomaly detection, and runbook copilots—always with explicit guardrails and observability
• Cross‑org influence: You align teams through fitness functions, scorecards, and readiness gates that accelerate—not block—delivery; you communicate tradeoffs crisply to execs and engineers
• AI fluency: Curious, adaptable, and proactive in exploring AI that responsibly improves scale outcomes
• (Desirable) Scale scorecard: Company‑wide fitness functions (latency/throughput/error rates) are adopted and reviewed regularly
• (Desirable) High‑impact wins: 2–3 bottlenecks removed with documented, reproducible testbeds; pXX latencies and error rates improve on top enterprise workloads; repeat P0s trend down
• (Desirable) AI‑assisted scale engineering: AI‑driven anomaly detection reduces alert noise while improving signal; generative load testing and copilot runbooks are used in release/readiness checks for the top critical services; time‑to‑isolate regressions drops 20–30%

What the job involves Be Klaviyo’s senior IC for scale, you will report into a VP of Engineering and lead performance, reliability, multi‑region, and large‑tenant readiness

• You’ll drive platform-wide architectural change, hunt bottlenecks and optimize systems, and partner across teams to productionize improvements
• Given that this is an IC role with no direct reports; you will lead via technical depth, hands‑on impact, and crisp cross‑org alignment
• Define enterprise scalability fitness functions (latency/throughput/error rates) and a scorecard; align teams to SLOs and budgets
• Design/implement sharding and partitioning strategies, caching/back‑pressure, multi‑region readiness, and high‑volume migration paths
• Build lightweight enablement: benchmarks, profiling harnesses, reproducible testbeds; pair with teams to land fixes
• Lead scalability reviews and readiness gates that accelerate—not block—delivery; drive incident deep dives tied to systemic fixes
• Communicate clearly to execs and engineers, tying technical work to business impact and customer outcomes
• Integrate AI into scale and resiliency work—from proactive anomaly detection to synthetic load and guided runbooks—so performance improvements stick and incidents don’t repeat

, Success in 6–12 Months: , Company‑wide scale scorecard in place; 2–3 high‑impact bottlenecks removed; top enterprise workloads show improved pXX latencies and error rates; fewer repeat P0s #J-18808-Ljbffr Klaviyo