Staff Software Engineer (Anywhere Cloud, AI Systems & Runtimes)

Cloudera's Anywhere Cloud team as a Staff Software Engineer. In this high-impact role, you will lead the architecture and delivery of our cloud-native AI platform, bridging the gap between cutting-edge AI research and production-grade Kubernetes environments. You will design and implement scalable application services, lead the deployment of inference servers, build internal tooling, and ensure AI workloads are secure and optimized for GPU resource scheduling.

About Cloudera

If you are passionate about Kubernetes, not just using it but building it at the core managing workloads across hybrid clouds and datacenters and obsessed with performance, devops, etc. this is where you belong

Responsibilities

• Lead the architecture and delivery of Cloudera's cloud-native AI platform, bridging the gap between AI research and production-grade environments.
• Design and implement scalable application services that wrap AI capabilities for enterprise use, ensuring serverless-style scaling for AI workloads.
• Collaborate with cross-functional teams to ensure the AI platform is powerful and highly usable for internal developers, while ensuring AI workloads are secure and optimized.
• Expert proficiency in Python (for AI ecosystem) and strong competence in a systems language like Go or Rust/C++ (for high-performance serving layers)
• Ability to navigate the rapidly changing AI landscape, filtering hype from practical engineering solutions, and driving technical alignment across teams

Requirements

• 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

Preferred

• Experience building complex workflows using tools like LangChain or LlamaIndex, and deploying them on containerized infrastructure (Docker/Kubernetes)
• Model Fine-Tuning: Experience with efficient fine-tuning techniques (PEFT, LoRA/QLoRA) on custom datasets
• Open Source: Contributions to open-source AI projects (HuggingFace transformers, vLLM, etc.)
• GPU Optimization: Familiarity with CUDA programming or profiling GPU performance (Nsight systems)

Benefits

• This role offers comprehensive benefits, including medical, dental, and vision plans, gym reimbursement, paid medical disability and child bonding leaves, and more.

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