Software Engineer - Cloud Engineering, Kubernetes

Kumo; Mountain View (CA); On-site; USD 98,000 - 126,000; Full time

Job summary

Join a forward-thinking company as a Software Engineer in Cloud Engineering, where you'll architect resilient Kubernetes infrastructure for a cutting-edge AI platform. This role offers the chance to work with advanced technologies in a collaborative environment, driving automation and enhancing scalability across multi-cloud platforms. You'll play a crucial role in shaping the future of cloud-native applications, ensuring high availability and performance. If you're passionate about Kubernetes and eager to make an impact, this opportunity is perfect for you!

Benefits

Competitive salary and equity options

Comprehensive medical and dental insurance

Inclusive and diverse work environment

Qualifications

• 5+ years of experience managing large-scale Kubernetes clusters.
• Expertise in cloud-native infrastructure across AWS, Azure, and GCP.
• Proficient in automating Kubernetes cluster management.

Responsibilities

• Design and scale Kubernetes infrastructure for AI workloads.
• Automate fleet management and optimize multi-cloud deployments.
• Collaborate with ML engineers to enhance resource allocation.

Skills

Kubernetes Management

Cloud-Native Infrastructure

Platform Engineering

Software Development

Infrastructure-as-Code

Distributed Systems

Education

BS in Computer Science

MS in Computer Science

PhD in Computer Science

Tools

Terraform

Ansible

Docker

Prometheus

Grafana

Job description

Join to apply for the Software Engineer - Cloud Engineering, Kubernetes role at Kumo

Join to apply for the Software Engineer - Cloud Engineering, Kubernetes role at Kumo

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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: 5-7+ 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: 5-7+ years of experience building cloud-native Kubernetes-based infrastructure across AWS, Azure, and GCP.
• Platform Engineering: 5-7+ 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: 5-7+ 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.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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