The Role
Architect and operate scalable Kubernetes infrastructure for AI workloads, manage multi-cloud deployments, automate processes, and enhance system reliability.
Summary Generated by Built In
About Kumo.ai
Kumo is building the infrastructure layer for the next generation of enterprise AI — a platform that lets organizations turn their data into predictive intelligence instantly, without the heavy lifting of traditional ML pipelines. We have also built our own Relational Foundation Model that can provide predictions in seconds – no training, straight to business value!
Join a dynamic, rapidly expanding team of innovators from top-tier companies like Airbnb, LinkedIn, Pinterest, and Stanford, supported by the renowned Sequoia Capital. We're on the front lines of AI, solving some of its most challenging and impactful problems, and we've already delivered over $500M+ in tangible value to industry giants like Reddit, DoorDash, and Databricks. If you thrive in a fast-paced environment, are driven by ambitious goals, and crave an opportunity for massive impact, this is your chance to shape the future of AI.
The Opportunity
We’re hiring a Lead / Staff+ Infrastructure Engineer to own the architecture, reliability, and evolution of Kumo’s multi-tenant AI platform. This is a hands-on leadership role: you’ll design high-leverage systems, make critical architectural decisions, mentor engineers, drive cross-functional roadmaps, and still spend a meaningful portion of your time writing code and running production services. If you’ve built large-scale cloud-native infrastructure, led cross-team infrastructure initiatives, and want to influence both product and platform at a technical and organizational level, this role is for you.
What You’ll Own
- Set the technical vision and roadmap for Kumo’s multi-tenant infrastructure across AWS, Azure, and GCP, balancing scalability, reliability, cost, and security.
- Lead architecture and design for critical systems: Kubernetes-based multi-tenancy, real-time inference clusters, training pipelines, and CI/CD for large ML workloads.
- Hands-on implementation: build and evolve IaC, GitOps flows, cluster autoscaling, and automation that reduce toil and accelerate developer productivity.
- Define and drive SLOs, SLIs, and capacity planning; lead incident response, postmortems, and systemic remediation.
- Own cost optimization at scale — from resource scheduling to spot/commit strategies and cross-cloud lifecycle management.
- Mentor and grow engineers: set standards for architecture reviews, design docs, code quality, and operational excellence.
- Hire and help scale the team — participate in recruiting, interviewing, and onboarding top-tier infrastructure talent.
What You Bring
- 5-8+ years building and operating production cloud-native infrastructure; proven track record leading infrastructure initiatives end-to-end.
- Deep, practical experience with Kubernetes at scale (multi-tenant environments, cluster federation, or large fleet operations).
- Strong multi-cloud operational experience (designing and running services across AWS/Azure/GCP) and cloud cost management.
- Demonstrated systems design skills for distributed systems, making architectural trade-offs and comfortable shipping code in a high-velocity environment (Python, Go, or similar) and reviewing complex PRs.
- Proficiency in Go, Python, Rust or similar languages for automation tooling.
- Excellent communicator: able to influence across engineering, ML science, product, and leadership — and to write clear design docs and trade-off analyses.
Nice to Have
- Experience building infrastructure for ML/AI platforms or relational foundation models.
- Background with Spark or large-scale data processing platforms (managed or self-hosted).
- Familiarity with Kubernetes operators, controllers, CRDs, or service mesh patterns.
- Expertise with Infrastructure-as-Code (Terraform/Pulumi) and GitOps (ArgoCD, Flux, Argo Workflows) in production.
- Experience with tenant isolation, zero-trust identity models, and cloud security/compliance frameworks.
- Prior experience building and scaling an infrastructure team (e.g., hiring, mentoring, org design).
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.
Top Skills
Ansible
Argo
AWS
Azure
Bash
Calico
CloudFormation
Docker
Envoy
Flux
GCP
Go
Grafana
Istio
Jenkins
Kubernetes
Make
Prometheus
Python
Rust
Terraform
Tigera
Traefik
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The Company
What We Do
Democratizing AI on the Modern Data Stack!
The team behind PyG (PyG.org) is working on a turn-key solution for AI over large scale data warehouses. We believe the future of ML is a seamless integration between modern cloud data warehouses and AI algorithms. Our ML infrastructure massively simplifies the training and deployment of ML models on complex data.
With over 40,000 monthly downloads and nearly 13,000 Github stars, PyG is the ultimate platform for training and development of Graph Neural Network (GNN) architectures. GNNs -- one of the hottest areas of machine learning now -- are a class of deep learning models that generalize Transformer and CNN architectures and enable us to apply the power of deep learning to complex data. GNNs are unique in a sense that they can be applied to data of different shapes and modalities.
