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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer, AI Frameworks - **Company:** NVIDIA Corporation - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $152,000.0 - $241,500.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, C++ (Programming Language), Nvidia CUDA, Software Debugging, Programming Tools, Distributed Computing Environment, Distributed Systems, Fault Tolerance, Python (Programming Language), Open Source Technology, Performance Tuning, Software Engineering, Systems Integration, Software Technical Review, Management of Software Versions, Data Logging, Pytorch, AI Platforms, Kubernetes, Information Technology, Low Latency, Free and Open-Source Software, Machine Learning Operations, Docker - **Published:** September 21, 2026 - **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Software-Engineer--AI-Frameworks_JR2015296 ## About the Role * BS/MS/PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience) * 5+ years of proven experience in related field * Hands-on experience integrating with at least one major AI framework/runtime (e.g., PyTorch, Ray, Triton Inference Server ecosystem, distributed runtimes, model serving stacks). * Solid understanding of AI workloads: model development basics, training vs. inference tradeoffs, and performance considerations (throughput/latency, batching, memory). * Experience with distributed systems concepts (RPC, scheduling, fault tolerance, resource management). * Practical Kubernetes experience: deploying and operating services/jobs, Helm/Kustomize, operators/controllers (nice to have), and debugging clusters. * Familiarity with containers and cloud-native tooling (Docker, container registries, CI/CD pipelines). * Strong software engineering experience in Go, C++ and/or Python, with a track record of shipping reliable systems. * Strong interpersonal skills and ability to collaborate across teams and with open-source communities. * Exceptional collaboration, communication, and documentation habits. Ways to stand out from the crowd: * Open-source contributions to Dynamo, PyTorch, Ray, llm-d, Kubernetes ecosystem, or related ML infrastructure projects. * Experience with large-scale model serving, distributed inference, or multi-tenant AI platforms. * Experience building SDKs/APIs or developer tooling that improves integration usability. * Knowledge of GPU performance profiling and optimization (Nsight tools or similar), and/or kernel-level performance tuning. * Experience with reproducibility, packaging, versioning, and compatibility testing across fast-moving dependencies. ## Description * Design and implement end-to-end integrations of Grove with open-source AI frameworks (e.g., Dynamo, llm-d, Ray, PyTorch, and related ecosystem projects). * Build and maintain adapters, plugins, operators, and/or runtime components that enable Grove features to work smoothly across training and inference stacks. * Partner with framework owners to upstream changes, contribute patches, and ensure long-term maintainability of integrations. * Develop reference workflows, sample apps, and best-practice guides that accelerate adoption by users and partners. * Optimize performance, scalability, and reliability for distributed training/inference, including multi-node and multi-GPU environments. * Improve observability and operational readiness (metrics, logging, tracing, debugging tools) for Kubernetes-based deployments. * Participate in technical design reviews, define APIs/contracts, and ensure compatibility across versions of frameworks and dependencies. * Diagnose complex issues spanning containers, networking, scheduling, CUDA/GPU utilization, and framework runtime behavior. ## Related Videos - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) - [Microservices: how to get started with Spring Boot and Kubernetes](https://www.wearedevelopers.com/videos/242-microservices-how-to-get-started-with-spring-boot-and-kubernetes) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [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)