Principal Software Engineer, Enterprise Scalability

What You’ll Do

• 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.

Who You Are

• 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.

Nice to Haves

• Scale scorecard: Company-wide fitness functions (latency/throughput/error rates) are adopted and reviewed regularly.
• 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.
• 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%.

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.

Pay

Base Pay Range For US Locations: $244,000—$366,000 USD

Schedule

This role may require up to 10% travel for purposes such as new hire onboarding, client or partner work if applicable, team meetings, and industry events. Travel is coordinated in advance.