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The Cloud Infrastructure team at Kumo is responsible for managing and scaling our Kubernetes-based, cloud-native AI platform across multiple cloud providers. They set service level objectives, optimize resource allocation, enforce security compliance, and drive cost efficiency for the Multi-Cloud Platform.
As a key team member, you will architect and operate a highly scalable, resilient Kubernetes infrastructure to support massive Big Data and AI workloads. You’ll design and implement advanced cluster management strategies, fleet capacity scaling, optimize workload scheduling, and enhance observability at scale. Your expertise in Kubernetes internals, networking, and performance tuning will be critical in ensuring high availability and seamless scaling.
Joining early, you'll play a pivotal role in shaping platform reliability, automating infrastructure, and enabling ML engineers with efficient commit-to-production automation, Continuous Provisioning, CI/CD, ML Ops, and deployment orchestration and workflows. You'll collaborate with ML scientists, product engineers, and leadership to influence scaling strategies, develop self-service tooling, and drive multi-cloud resilience. Engineers at Kumo take ownership of core system design, building infrastructure that powers the next generation of AI applications.
Key Responsibilities
- Design, build, and scale Kubernetes-based infrastructure to support Kumo’s multi-cloud AI platform, ensuring high availability, resilience, and performance.
- Architect and optimize large-scale Kubernetes clusters, improving scheduling, networking (CNI), and workload orchestration for production environments.
- Develop and extend Kubernetes controllers and operators to automate cluster management, lifecycle operations, and scaling strategies.
- Enhance observability, diagnostics, and monitoring by building tools for real-time cluster health tracking, alerting, and performance tuning.
- Lead efforts to automate fleet management, optimizing node pools, autoscaling, and multi-cluster deployments across AWS, GCP, and Azure.
- Define and implement Kubernetes security policies, RBAC models, and best practices to ensure compliance and platform integrity.
- Collaborate with ML engineers and platform teams to optimize Kubernetes for machine learning workloads, ensuring seamless resource allocation for AI/ML models.
- Drive commit-to-production automation, cloud connectivity, and deployment orchestration, ensuring seamless application rollouts, zero-downtime upgrades, and global infrastructure reliability.
Required Skills and Experience
- Kubernetes Mastery: 8-10+ years of experience managing large-scale Kubernetes clusters (EKS, GKE, AKS, or OpenSource) in production. Deep expertise in Kubernetes internals, including controllers, operators, scheduling, networking (CNI), and security policies.
- Cloud-Native Infrastructure: 8-10+ years of experience building cloud-native Kubernetes-based infrastructure across AWS, Azure, and GCP.
- Platform Engineering: 8-10+ years of experience building Kubernetes service meshes (Istio/Envoy, Traefik), networking policies (Calico/Tigera), and distributed ingress/egress control.
- Fleet Management & Scaling: Proven experience in optimizing, scaling, and maintaining Kubernetes clusters across multi-cloud environments, ensuring high availability and performance.
- Software Development: 8-10+ years of experience writing production-grade controllers and operators in Python, Go, or Rust to extend Kubernetes functionality.
- Infrastructure-as-Code & Automation: Hands-on experience with Terraform, CloudFormation, Ansible, BASH and Make scripting to automate Kubernetes cluster provisioning and management.
- Distributed Systems & SaaS: Expertise in building and operating large-scale distributed systems for cloud-native B2B SaaS applications running on Kubernetes.
- Cloud Application Deployment: Deep expertise in building of container orchestration, workload scheduling, and runtime optimizations using Kubernetes, Argo or Flux.
- Education: BS/MS in Computer Science or a related field (PhD preferred)
Nice to Have
- Proficiency with cloud platforms such as AWS, GCP, or Azure.
- Familiarity with chaos engineering tools and practices for testing system resilience.
- Strong understanding of security best practices and compliance standards (GDPR, SOC2, ISO27001, vulnerability assessments, GRC, risk management).
- Contributions to open-source projects, particularly in the Kubernetes or cloud-native ecosystem.
- Expertise in Docker, Kubernetes, Jenkins, Flux, Argo, and Terraform in a Linux environment.
- Hands-on experience with monitoring and observability tools such as Prometheus and Grafana.
- Ability to develop customer-facing web frontends or public APIs/SDKs for platform services.
Benefits
- Competitive salary and equity options.
- Comprehensive medical and dental insurance.
- An inclusive, diverse work environment where all employees are valued and supported.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Kumo Mountain View, California, USA Office
357 Castro St, Suite 200, Mountain View, CA, United States, 94041
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
