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Were looking for engineers who are passionate about operating system internals, large-scale distributed systems, and cloud security and who want to make a meaningful impact by building robust, high-performance security platforms. This is also a great opportunity to take ownership, lead initiatives, and grow through collaboration, mentorship, and technical leadership.

Our Stack: Python, Linux & Windows internals, Container Runtimes, Postgres, Redis, Kafka, AWS, GCP, Azure

On a typical day youll:
Design, implement, and maintain scalable backend services for scanning and analyzing cloud workloads (VMs, containers, buckets, databases, etc..)
Build infrastructure for reconstructing file systems across different operating systems (Linux and Windows) to enable deep analysis
Integrate security detection engines for vulnerabilities, secrets, compliance, and malware
Collaborate with security researchers and product managers to translate complex requirements into impactful product features
Write clean, efficient, and testable code, ensuring high performance and reliability
Participate in design and code reviews to uphold technical excellence and team standards
Lead features end-to-end from design and planning to deployment and monitoring
Improve system observability, performance, and resilience in production environments
Stay current with developments in the cloud security landscape, vulnerability management, and OS internals

Were hiring an Optimization Algorithm Developer to own the algorithmic core of our cloud cost optimization solutions. Youll design optimization strategies, apply predictive and machine learning methods where they add value, and work with backend and data engineers to embed algorithms into production.

This is a hands-on role for someone who enjoys solving complex cost/performance trade-offs at scale and wants to see their algorithms drive tangible impact for customers.


Your ownership will include:

Develop and refine optimization algorithms for cloud usage and workload management.
Use statistical modeling, predictive techniques, and machine learning to analyze patterns and anticipate future behavior.
Deliver practical solutions that provide value early and can evolve into production-grade algorithms.
Define data and integration requirements; collaborate with backend engineers to operationalize solutions.
Establish clear evaluation methods (backtesting, offline experiments, KPIs) to measure performance.
Stay current with cloud economics, Kubernetes scaling practices, and FinOps trends.