Sr. Backend Engineer II, AutoScaling & Balancing, NG-SIEM (Hybrid)

As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. CrowdStrikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation. We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you.

Our mission is to make all of our customers’ security-relevant data continuously available for automated detection and response, threat hunting, and other Falcon use cases. To enable this, the systems behind NG-SIEM are growing to accommodate >100 PB of event and action data ingested every day, up to 10 years of retention, and dozens of millions of queries per hour across large sections of the data stored. Ideal candidate for this Senior Engineer II has deep expertise in large-scale distributed/control systems (intelligent placement, auto balancing, autoscaling) and the ability to go deep into the underlying data platform (LogScale) — ingestion, storage, and query internals — to connect scaling decisions to real system behavior in a large multi-tenant environment. We run a multi-tenant platform where many customers share a common pool of infr astructure. The core challenge is a two-sided optimization: deliver the best performance for every customer while achieving the best overall resource utilization (minimal wasted capacity, maximum density). This role is hybrid, on-site at one of the posted locations: Sunnyvale, CA; Redmond, WA; New York City; or Austin, TX with a strong preference to candidates in Austin, TX.  What You’ll Do: Intelligent Customer Placement — Decide which part of the fleet each customer lands on, bin packing tenants efficiently while reserving enough headroom so everyone gets the capacity they're entitled to. Auto balancing — Continuously rebalance customers across shared infrastructure as load shifts, protecting each customer's performance while keeping the fleet healthy. Autoscaling — Automatically add or remove capacity as demand changes, so the fleet grows and shrinks with real usage instead of being manually provisioned. Data-Driven Decisions — Use real-time per-tenant usage (e.g., memory, storage, throughput, query cost) to drive placement and balancing. End-to-End System Depth — Go deep into the underlying data platform (how data is ingested, stored, cached, and queried) to understand why a placement or scaling decision helps or hurts, and feed that back into the models. Operational Rigor — Operate what you build, and treat customer-impacting incidents with the same rigor as feature work; reliability and static stability are forcing functions for design, not a phase bolted on after launch. Feature Delivery — Deliver key parts of our systems as an individual contributor while leading the team to solve ambiguous problems, and move the scalability of our LogScale engine to the next level. Technical Strategy & Alignment — Engage with other technical leads and principal engineers to keep our