Software Engineer Lead - Cloud Engineering at Kumo

Mountain View, California, United States

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Not SpecifiedCompensation

Expert & Leadership (9+ years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Big Data, Cloud ComputingIndustries

Requirements

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
8-10+ years of experience building cloud-native Kubernetes-based infrastructure across AWS, Azure, and GCP
8-10+ years of experience building Kubernetes service meshes (Istio/Envoy, Traefik), networking policies (Calico/Tigera), and distributed ingress/egress control
Proven experience in optimizing, scaling, and maintaining Kubernetes clusters across multi-cloud environments, ensuring high availability and performance
8-10+ years of experience writing production-grade controllers and operators in Python, Go, or Rust to extend Kubernetes functionality
Hands-on experience with Terraform, CloudFormation, Ansible, BASH and Make scripting to automate Kubernetes cluster provisioning and management
Expertise in building and operating large-scale distributed systems for cloud-native B2B SaaS applications running on Kubernetes
Deep expertise in building container orchestration, workload scheduling, and runtime optimizations

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

Skills

Key technologies and capabilities for this role

KubernetesMulti-CloudCNIKubernetes OperatorsControllersCI/CDMLOpsObservabilityNetworkingPerformance TuningCluster ManagementWorkload Orchestration

Questions & Answers

Common questions about this position

What experience level is required for this role?

The role requires 8-10+ years of experience managing large-scale Kubernetes clusters and building cloud-native Kubernetes-based infrastructure across AWS, Azure, and GCP.

What are the key technical skills needed for this position?

Key skills include deep expertise in Kubernetes internals such as controllers, operators, scheduling, networking (CNI), and security policies, plus experience with large-scale clusters on EKS, GKE, AKS, or OpenSource across AWS, Azure, and GCP.

Is this a remote position or does it require office work?

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

What kind of impact will I have joining this team early?

Joining early, you'll play a pivotal role in shaping platform reliability, automating infrastructure, enabling ML engineers with efficient commit-to-production automation, and collaborating with ML scientists, product engineers, and leadership to influence scaling strategies.

Kumo

Generates and deploys predictive models

About Kumo

Kumo.ai specializes in creating and implementing accurate predictive models for organizations that need reliable forecasts for critical operations. Their platform uses Graph Neural Networks to analyze raw relational data, which removes the need for manual data preparation and enhances prediction accuracy and efficiency. Unlike many competitors, Kumo.ai's platform streamlines the entire Machine Learning lifecycle, from data preparation to model deployment, while also optimizing costs by eliminating unnecessary infrastructure. The company aims to provide a quick return on investment for its clients, which range from small businesses to large enterprises, by offering flexible deployment options through Software as a Service (SaaS) and Private Cloud models. Kumo.ai is built by experienced professionals from top tech companies and has already gained the trust of leading organizations globally.

Mountain View, CaliforniaHeadquarters

2021Year Founded

$35.5MTotal Funding

SERIES_BCompany Stage

Fintech, AI & Machine LearningIndustries

51-200Employees

Benefits

Stock Options

Medical Insurance

Dental Insurance

Risks

Increased competition from Databricks' Marketplace may divert potential customers.

The rise of multimodal AI could overshadow Kumo's current offerings.

Rapid AI advancements by tech giants may set new industry standards Kumo must meet.

Differentiation

Kumo.AI uses Graph Neural Networks for predictive modeling, eliminating manual feature engineering.

The platform offers a SQL-like Predictive Querying Language for rapid AI model creation.

Kumo.AI integrates with Snowflake's Native App Framework, enhancing model performance and scalability.

Upsides

Kumo's $18M Series B funding will expand its platform and market reach.

Integration with Snowpark Container Services enhances deep learning capabilities within Snowflake Data Cloud.

Kumo's platform supports both SaaS and Private Cloud models, offering client flexibility.

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