ScaleOps, the leader in real-time automated cloud resource management, is redefining the way engineering teams run in the cloud. Our platform automatically allocates resources to match real-time demand, achieving 60–80% cost savings while improving performance and simplifying DevOps operations.
Backed by $80M from top-tier investors, and trusted by leading cloud-native innovators such as Wiz, CATO Networks, SentinelOne, and Orca Security, we’re rapidly expanding our core technology team.
We are looking for a Researcher who thrives on exploring the technical frontier and translating complex insights into real-world impact. This is a hands-on role, combining deep research with prototyping and cross-team collaboration, with the goal of driving innovation in Kubernetes internals, cloud infrastructure, and performance optimization.
What You’ll Be Doing
Conduct deep research into Kubernetes internals, scheduling, autoscaling, and container resource usage.
Explore cutting-edge strategies for cloud resource optimization, cost efficiency, and performance engineering.
Design and deliver hands-on POCs, simulations, and benchmarks to validate new approaches.
Work closely with product and engineering teams to translate research findings into features and enhancements.
Stay at the forefront of the cloud-native and FinOps ecosystems, surfacing trends and opportunities.
Contribute to technical blogs, white papers, and internal knowledge sharing.
דרישות
7+ years of experience in engineering, systems research, or large-scale infrastructure roles.
Deep knowledge of Kubernetes internals, containerization, cloud infrastructure, and resource orchestration.
Hands-on coding experience in Go, Python, or other systems-level programming languages.
Strong analytical and research skills, with a track record of building and delivering innovative POCs.
Ability to thrive in uncertainty, take ownership of technical domains, and drive projects end-to-end.
Excellent written and verbal communication skills.
Big pluses: background in cloud cost optimization, scheduling, or autoscaling; published technical research artifacts; experience presenting on technical stages; machine learning experience (e.g., time-series models, LLMs); or prior security research.