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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer, Generative AI Systems - **Company:** NVIDIA Ltd. - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $152,000.0 - $287,500.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Automation of Tests, Microsoft Azure, C++ (Programming Language), Cloud Computing, Computer Clusters, Computer Programming, Computer Engineering, Continuous Integration, Software Debugging, Programming Tools, Distributed Systems, Fault Tolerance, Graph Database, Python (Programming Language), Knowledge Management, Machine Learning, Node.Js, Tensorflow, Azure Machine Learning, Software Safety, Software Construction, Software Deployment, Software Engineering, TypeScript, AI Infrastructure, Cloud Platform System, Pytorch, Large Language Models, Prompt Engineering, Generative AI, Backend, Fastapi, AI Platforms, Kubernetes, Information Technology, Machine Learning Operations, Virtual Agents, Api Design, Docker - **Published:** May 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9362dcc4dd475ea0 ## About the Role Do you have experience in Software engineering?, * BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, or related technical field (or equivalent experience). * Minimum of 2+ years of related industry experience in software engineering, AI/ML systems, distributed systems, cloud infrastructure, or Generative AI applications. * Strong programming skills in Python and/or C++ with experience building scalable software systems. * Experience developing distributed systems, cloud infrastructure, backend services, or ML systems infrastructure. * Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, JAX, or DeepSpeed. * Experience with Kubernetes, Docker, and cloud platforms such as AWS, GCP, or Azure. * Familiarity with large language models (LLMs), RAG systems, prompt engineering, evaluation frameworks, or agentic AI workflows. * Experience building APIs and scalable services using frameworks such as FastAPI, Node.js, TypeScript, or related technologies. * Strong understanding of software engineering best practices including CI/CD, automated testing, debugging, observability, and production system reliability. Ways to Stand Out from the Crowd: * Experience building infrastructure for distributed ML training or large-scale inference systems. * Background in high-performance distributed systems, GPU scheduling, or fault-tolerant training architectures. * Experience developing LLM evaluation frameworks, AI safety systems, hallucination detection pipelines, or agentic AI benchmarking platforms. * Familiarity with knowledge graphs, retrieval systems, vector databases, or scalable RAG architectures. * Experience building Kubernetes-based ML platforms, asynchronous evaluation systems, or cloud-native AI infrastructure. ## Description You will work closely with cross-functional teams to build scalable AI infrastructure, develop robust evaluation methodologies, and improve the reliability, safety, and performance of production AI services. The ideal candidate combines strong software engineering fundamentals with hands-on experience in machine learning systems, distributed infrastructure, and modern GenAI workflows. What You'll Be Doing: * Design and develop scalable infrastructure for large-scale ML training, inference, and Generative AI systems. * Build distributed systems and cloud-native platforms supporting GPU clusters, fault-tolerant training, and high-performance AI workloads. * Develop evaluation frameworks for LLMs and agentic AI systems, including hallucination detection, safety validation, robustness testing, and tool-calling reliability. * Architect and optimize retrieval-augmented generation (RAG) pipelines, knowledge management systems, and scalable AI data workflows. * Build backend services, APIs, and production AI infrastructure using technologies such as FastAPI, Kubernetes, Docker, and modern cloud platforms. * Develop automated benchmarking, orchestration, and asynchronous processing systems for enterprise AI applications and evaluation platforms. * Collaborate cross-functionally with research, product, and engineering teams to improve scalability, reliability, observability, and developer productivity across AI systems. * Contribute to full-stack AI applications, developer tooling, and production deployment pipelines supporting next-generation AI-powered workflows. ## Related Videos - [Your Next AI Needs 10,000 GPUs. Now What?](https://www.wearedevelopers.com/videos/1590-your-next-ai-needs-10-000-gpus-now-what) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)