Cast AI
201 - 500 employees
Founded 2019
☁️ SaaS
🤖 Artificial Intelligence
🤝 B2B
SaaS • Artificial Intelligence • B2B
Cast AI is a SaaS platform that automates Kubernetes application performance, infrastructure, and cost optimization for cloud-native teams. It connects to EKS/AKS/GKE and on-prem clusters with no code changes, uses predictive models and runtime signals to rightsize pods, scale nodes, optimize GPUs and Spot instances, and run approved self-healing runbooks to maintain reliability and reduce cloud spend. Cast AI targets platform, SRE, and FinOps teams and supports AI/LLM workloads, GPU optimization, and real-time workload placement and cost intelligence.
Senior Software Engineer – Kubernetes Automation
🔥 1 hour ago
Where this job comes from
Posted on Cast AI's careers page, not through a job board
First seen by Remote Rocketship 1 hour ago
Still open on Cast AI's site. Checked 14 minutes ago.
Cast AI
201 - 500 employees
Founded 2019
☁️ SaaS
🤖 Artificial Intelligence
🤝 B2B
SaaS • Artificial Intelligence • B2B
Cast AI is a SaaS platform that automates Kubernetes application performance, infrastructure, and cost optimization for cloud-native teams. It connects to EKS/AKS/GKE and on-prem clusters with no code changes, uses predictive models and runtime signals to rightsize pods, scale nodes, optimize GPUs and Spot instances, and run approved self-healing runbooks to maintain reliability and reduce cloud spend. Cast AI targets platform, SRE, and FinOps teams and supports AI/LLM workloads, GPU optimization, and real-time workload placement and cost intelligence.
📋 Description
• Design and build distributed systems that operate Kubernetes infrastructure autonomously at scale. • Write production Go services interacting with AWS, GCP, and Azure APIs for real-time cloud resource management. • Own features end-to-end from design through implementation, testing, and production rollout. • Debug complex production issues across cloud providers, Kubernetes clusters, and distributed services. • Collaborate with product and engineering teams on problems without textbook solutions. • Work with time-series data, cloud provider APIs, and Kubernetes control plane internals. • Contribute to product direction and backlog. • Participate in on-call rotation for teams that require it. • Work across node placement and provisioning, Karpenter integrations, workload rightsizing, workload movement, self-scaling storage, and GPU capacity optimization.
🎯 Requirements
• Strong software engineering fundamentals, including clean abstractions, maintainable code, and justified architecture choices. • Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. • Understanding of Kubernetes internals, including autoscaling and networking. • Personally driven a complex project end-to-end. • Experience with AI coding agents and agentic development workflows. • Used observability tooling in production. • Experience with CI/CD and DevOps practices. • Strong verbal and written English skills. • Startup mindset: adaptable, proactive, and comfortable with ambiguity. • Deep hands-on experience with cloud platforms such as AWS, GCP, or Azure, including compute, networking, and storage knowledge. • Deep knowledge of EKS, GKE, or AKS internals. • Container runtime experience with CRI-O, runc, or containerd. • Cloud storage knowledge, including block storage, CSI, volume management, and filesystem internals. • Low-level Linux experience with process internals, packet-level networking, filesystems, and storage. • Low-level systems programming experience in C or C++. • Kubernetes or cloud-native OSS contributions.
🏖️ Benefits
• Enjoy a flexible, remote-first global environment. • Equity options. • Spend 10% of your work time on personal projects or self-improvement. • Learning budget for professional and personal development, including access to international conferences and courses. • Annual hackathon. • Team-building budget and company events. • Equipment budget. • Extra days off. • Quick feedback with a fast-paced workflow; most feature projects are completed in 1 to 4 weeks.
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