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Software Engineer - AI & Edge Kubernetes Orchestration - San Jose, CA at ZEDEDA Inc · Katori

Asks for

The ad says“Foundational knowledge of machine learning concepts: neural networks, deep learning, model training and inference, and attention mechanisms (self-attention / transformers).”
The ad says“Foundational knowledge of machine learning concepts: neural networks, deep learning, model training and inference, and attention mechanisms (self-attention / transformers).”
The ad says“Familiarity with ONNX models, GenAI model architectures, or frameworks like PyTorch or TensorFlow.”
The ad says“Comfort working with Git, submitting pull requests, reading diffs, and collaborating in a version-controlled environment.”
The ad says“Foundational knowledge of machine learning concepts: neural networks, deep learning, model training and inference, and attention mechanisms (self-attention / transformers).”
The ad says“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.”
The ad says“Comfortable with basic Linux commands and shell scripting.”
The ad says“Foundational knowledge of machine learning concepts: neural networks, deep learning, model training and inference, and attention mechanisms (self-attention / transformers).”
The ad says“Foundational knowledge of machine learning concepts: neural networks, deep learning, model training and inference, and attention mechanisms (self-attention / transformers).”
The ad says“Foundational knowledge of machine learning concepts: neural networks, deep learning, model training and inference, and attention mechanisms (self-attention / transformers).”
The ad says“Familiarity with ONNX models, GenAI model architectures, or frameworks like PyTorch or TensorFlow.”
The ad says“Comfortable with basic Linux commands and shell scripting.”

Values

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

About ZEDEDA
ZEDEDA unlocks the value of AI where it matters most, enabling enterprises to create, secure and operate edge AI at scale. ZEDEDA’s Edge Intelligence products and solutions are used by global distributed enterprises to rapidly realize and deploy autonomous intelligence wherever they operate, turning real-time data into real and tangible business outcomes and decisions. Trusted by the world’s largest organizations, ZEDEDA is backed by world-class investors, with teams in the United States, Germany, India, and the United Arab Emirates. For more information, visit www.ZEDEDA.ai.

Location: San Jose, CA (3 days onsite)

This is an onsite position based in our San Jose, CA office. Candidates must be able to reliably commute to this location; relocation assistance and travel/commuting expenses are not provided.

Role Summary

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.

Core 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.

Qualifications:

Required:

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.

Preferred:

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.

Pay & Benefits

At ZEDEDA, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. Base pay is determined by considering your skills, qualifications, experience, and location. For this role the base pay range is $120,000-$140,000

Why ZEDEDA

ZEDEDA offers competitive salary, performance-based bonuses, comprehensive medical benefits, hybrid work flexibility, and meaningful opportunities for technical growth and advancement. Engineers at every level have access to AI productivity tools, on the job learning, and a culture that celebrates curiosity and experimentation - because at ZEDEDA, impact matters more than activity, and learning is never optional.