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# Staff Software Engineer

- **Company:** Cloudradiant Corp.
- **Location:** San Jose, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $184,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Nvidia CUDA, Computer Programming, Python (Programming Language), Node.Js, Open Source Technology, Software Engineering, Graphics Processing Unit (GPU), Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, AI Platforms, Kubernetes, ONNX (Open Neural Network Exchange) Format, HuggingFace, Machine Learning Operations, GPT, Serverless Computing, Docker
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/staff-software-engineer-anywhere-cloud-ai-systems-runtimes-cloudera-9871909

## About the Role

* Bachelor's degree with 6+ years of software engineering experience (or equivalent Masters/PhD tenure), with at least 2+ years focused on AI/ML systems.
 * Expert proficiency in Python (for AI ecosystem) and strong competence in a systems language like Go or Rust/C++ (for high-performance serving layers).
 * Deep understanding of LLM deployment challenges and runtimes (e.g., vLLM, ONNX, TorchServe, Triton). Familiarity with quantization techniques (AWQ, GPTQ) to optimize model size/speed.
 * Experience building complex workflows using tools like LangChain or LlamaIndex, and deploying them on containerized infrastructure (Docker/Kubernetes).
 * Ability to navigate the rapidly changing AI landscape, filtering hype from practical engineering solutions, and driving technical alignment across teams.

You May Also Have:

 * Model Fine-Tuning: Experience with efficient fine-tuning techniques (PEFT, LoRA/QLoRA) on custom datasets.
 * GPU Optimization: Familiarity with CUDA programming or profiling GPU performance (Nsight systems).
 * Open Source: Contributions to open-source AI projects (HuggingFace transformers, vLLM, etc.).

## Description

* Enterprise AI Services: Design and implement elegant, scalable application services (Go/Node.js) that wrap AI capabilities for enterprise use.
 * K8s-Native AI Orchestration: Lead the deployment of inference servers (vLLM, Triton) using KServe, KubeRay, or Knative to ensure serverless-style scaling for AI workloads.
 * Developer Velocity: Build internal tooling, SDKs, and "AI Gateways" that enhance team agility and simplify the integration of Foundation Models (Llama, GPT) into product features.
 * RAG & Prompt Engineering: Architect robust Retrieval-Augmented Generation (RAG) pipelines and prompt management services that integrate seamlessly with vector databases and enterprise data sources.
 * Cross-Functional Collaboration: Partner with UI engineers, UX designers, and Product Management to ensure the AI platform is not just powerful, but highly usable for internal developers.
 * Infrastructure & Security: Ensure AI workloads are secure, multi-tenant, and optimized for GPU resource scheduling (MIG, fractional GPUs) within Kubernetes.


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