Researcher

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.
  
  

• 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.