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PointFive is a cloud cost intelligence platform used by Fortune 500 companies (Fanatics, H&M, Hertz, Nubank, Citizens Bank) to identify and eliminate waste in enterprise infrastructure and AI spending. The company has raised $96M (Series B led by Accel), with engineering based in Tel Aviv and go-to-market focused on North America. PointFive Labs is the research arm publishing original work on infrastructure and AI cost dynamics.
You will own Kubernetes cost efficiency end-to-end as a researcher, moving from identifying expensive patterns through billing and metrics analysis to shipping detections adopted by thousands of engineering teams. This role sits at the intersection of research, product, and engineering—you identify problems, validate them against real customer data, and drive them to production.
Key responsibilities:
- Research and ship new Kubernetes cost-efficiency detections by analyzing real customer data and identifying optimization opportunities
- Lead the Kubernetes domain end-to-end: own the detection library and roadmap to maximize customer value
- Work closely with engineering teams, diving deep into Kubernetes internals, cloud provider APIs, billing systems, and data pipelines as needed
- Engage directly with prospects and customers in pre-sales and post-sales to maximize value realization
- Publish thought leadership on Kubernetes cost efficiency through blog posts, white papers, open-source contributions, webinars, and live sessions
You'll set standards and best practices for Kubernetes efficiency that engineering teams globally will adopt. Your detections will run across complex Kubernetes environments on every major cloud provider, and your research will inform how thousands of teams optimize their infrastructure.
REQUIREMENTS:
- 3+ years running production Kubernetes workloads (EKS, GKE, or AKS) as an SRE, platform, DevOps, or infrastructure engineer. Must have on-call experience and deep understanding of requests/limits, scheduling, pod packing, cluster autoscaler behavior, Karpenter, node group sizing, and instance architecture impacts.
- Ability to own ambiguous problems end-to-end from hypothesis to shipped detection
- Skill in orchestrating engineering work: translating research into clear technical requirements, aligning priorities, and coordinating delivery
- Deep, hands-on AWS knowledge (EC2, EKS, ECR, networking, Savings Plans, RIs) plus working knowledge of Azure or GCP
- Strong quantitative skills: analytics, statistics, modeling. Ability to work with complex unstructured data and validate results rigorously
- Evidence-first, skeptical mindset focused on finding flaws in recommendations
- Clear communication skills for explaining technical findings and business impact to diverse stakeholders
NICE TO HAVE:
- Experience with AI/ML infrastructure (GPU node pools, inference serving, managed platforms like Bedrock/Azure OpenAI/Vertex AI, per-token and provisioned-throughput pricing)
- FinOps experience (AWS CUR, Split Cost Allocation Data, FOCUS, cost allocation, chargeback)
- Hands-on dbt, Snowflake, or Go experience
- ARM/Graviton migration or large-scale rightsizing program experience
- Data platform experience (Databricks, Snowflake, BigQuery)
- Published technical research (blog, white papers, OSS) or public speaking experience
- Open-source contributions
- Cloud security research background
- Academic background
