Principal Engineer - Enterprise Scale & AI

Requirements

• Experience:
  
  12+ years scaling multi‑tenant SaaS with a reputation for removing major bottlenecks and proving impact with data
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• Technical expertise:
  Performance engineering, capacity planning, sharding/partitioning, caching/back‑pressure, multi‑region readiness, and high‑volume migrations; you turn hotspots into robust patterns
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• 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
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• 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
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• AI fluency:
  Curious, adaptable, and proactive in exploring AI that responsibly improves scale outcomes
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• (Desirable) Scale scorecard:
  Company‑wide fitness functions (latency/throughput/error rates) are adopted and reviewed regularly
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• (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
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• (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%

• 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
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• You’ll drive platform-wide architectural change, hunt bottlenecks and optimize systems, and partner across teams to product ionize improvements
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• 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
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• Define enterprise scalability fitness functions (latency/throughput/error rates) and a scorecard; align teams to SLOs and budgets
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• Design/implement sharding and partitioning strategies, caching/back‑pressure, multi‑region readiness, and high‑volume migration paths
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• Build lightweight enablement: benchmarks, profiling harnesses, reproducible testbeds; pair with teams to land fixes
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• Lead scalability reviews and readiness gates that accelerate—not block—delivery; drive incident deep dives tied to systemic fixes
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• Communicate clearly to execs and engineers, tying technical work to business impact and customer outcomes
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• 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
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• Success in 6–12 Months:
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• 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