Software Engineer - AI & Edge Kubernetes Orchestration - San Jose, CA

ZEDEDA•San Jose, CA

134d•$120,000 - $140,000•Hybrid

About The Position

This position is not eligible for visa sponsorship. Applicants must be authorized to work in the United States without employer sponsorship, now and in the future. We're looking for a curious, self-driven entry level Software Engineer who sits at the intersection of AI and cloud-native infrastructure. You'll work alongside experienced engineers on real-world problems in edge orchestration - problems that are often loosely defined, fast-moving, and require you to think from first principles. You bring energy, adaptability, and a genuine enthusiasm for using AI tools and technologies, both as the subject of your work and as instruments in how you work every day. This role for a recent graduate or someone with up to two years of industry experience. You won't be handed a perfectly scoped ticket - you'll be trusted to figure things out.

Requirements

Bachelor's or Master's degree in Computer Science, AI/ML, or a related technical field — or equivalent practical experience.
Foundational knowledge of machine learning concepts: neural networks, deep learning, model training and inference, and attention mechanisms (self-attention / transformers).
Familiarity with ONNX models, GenAI model architectures, or frameworks like PyTorch or TensorFlow.
Practical exposure to Kubernetes — understanding of pods, deployments, services, namespaces, and controllers. Familiarity with lightweight Kubernetes distributions such as k3s is a plus, particularly in the context of resource-constrained edge environments.
Comfort working with Git, submitting pull requests, reading diffs, and collaborating in a version-controlled environment.
Ability to work with vague or evolving problem statements and drive toward clarity independently.
Language-agnostic development mindset — you pick the right tool for the job and learn what you don't know.
Comfortable with basic Linux commands and shell scripting.

Nice To Haves

Hands-on experience with Kubernetes advanced constructs: Custom Resource Definitions (CRDs), Operators, Controllers, and the kubeconfig API.
CKA (Certified Kubernetes Administrator) or CKD certification, or active preparation for it.
Experience with AI agent frameworks: LangChain, LangGraph, LangFuse, or similar.
Demonstrated use of AI coding tools (Claude Code, GitHub Copilot, OpenAI Codex) in real development workflows — not just familiarity, but fluency.
Prior contribution to, or porting of, open-source projects.
Experience with CI/CD systems: Jenkins, CircleCI, GitHub Actions, or similar.
Familiarity with AWS or Azure tooling.
Knowledge of cloud-native technologies: Kafka, REST APIs, SSO/OAuth, microservices patterns.
Exposure to Helm chart authoring, not just usage.
Awareness of edge computing concepts, IoT, or distributed systems.
Familiarity with edge AI hardware platforms and inference infrastructure: NVIDIA Jetson (Jetpack SDK), Qualcomm IQ9, NVIDIA Triton Inference Server, vLLM, or similar model serving frameworks.
Familiarity with ArgoCD or other GitOps-based continuous delivery tools for Kubernetes.

Responsibilities

Design, develop, and maintain software components that bridge AI model lifecycle management with Kubernetes-based edge orchestration.
Build and extend Kubernetes controllers, operators, and Custom Resource Definitions (CRDs) to support AI workload scheduling and deployment at the edge.
Work with ONNX, GenAI, and ML models — integrating them into production-ready pipelines and edge environments.
Use AI coding agents (Claude Code, Copilot, Codex, etc.) as first-class tools in your daily development workflow.
Participate in design discussions, write clean code, submit pull requests, and iterate rapidly based on feedback.
Contribute to open-source components related to ZEDEDA's platform and the broader cloud-native ecosystem.
Write and maintain Helm charts for deploying services into Kubernetes clusters.
Collaborate with cross-functional teams across AI, infrastructure, and product to ship features end-to-end.