Senior Product Dev Rel Engineer - DSX
NVIDIA (Eightfold)
- Location
- US, CA, Santa Clara
- Work model
- On-Site
- Level
- Senior
- Posted
- Aug 19, 2026
Skills
About this role
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Become part of NVIDIA’s groundbreaking efforts to transform computer graphics, PC gaming, and accelerated computing. As a Senior Staff Product Developer Relations Engineer - DSX, you will advance the developer experience across NVIDIA’s Data Science & AI System Software platform (DSX). In this high-impact role, you will bridge the gap between external AI developers, cloud infrastructure builders, and internal DSX software and platform engineering teams. You will play a vital role in crafting the usability, performance, and developer ergonomics of our deep learning software stack, container runtimes, system orchestration, and AI platform tooling. Join a team where your technical leadership will directly accelerate how enterprise developers and researchers build and deploy AI applications on NVIDIA infrastructure!
What you'll be doing
Driving developer-centric product improvements for DSX by collecting and synthesizing technical feedback, system-level friction, and workflow requirements from AI practitioners, enterprise data platform teams, and open-source communities into DSX Product Management and Core Engineering roadmaps. Architecting and publishing high-impact technical assets, including production-grade deployment guides, containerized workflows, reference architectures, and end-to-end DSX benchmarks showcasing efficient performance across NVIDIA GPU clusters. Collaborating across departments including DSX Engineering, DGX Cloud, Solution Architecture, and Developer Marketing to steer early-access (EA) programs and validate core DSX software stack releases against real-world AI workloads prior to production launches. Conducting technical deep-dives and system architecture reviews with strategic ISV partners, enterprise AI engineering teams, and cloud platform providers to remove friction and accelerate adoption of DSX components. Defining measurements for developer engagement within DSX that track system software usability, setup ergonomics, driver/runtime integration, documentation clarity, and time-to-first-training run. Representing NVIDIA DSX as a technical authority at major AI, cloud-native, and developer conferences, building strong feedback loops that increase platform velocity and developer trust. What we need to see: Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent experience. 12+ years of proven experience in Developer Relations, Systems Engineering, Technical Product Management, or Solutions Architecture passionate about enterprise AI infrastructure, cloud platforms, or developer platforms. Deep technical foundation in Linux systems, Python, C++, GPU acceleration, container orchestration (e.g., Kubernetes, Docker), and modern AI framework stacks (e.g., PyTorch, CUDA). Established experience in guiding platform software and engineering priorities through detailed developer research, infrastructure troubleshooting, and product feedback loops. Hands-on experience building, deploying, or optimizing enterprise data science environments, containerized AI workloads, or distributed deep learning pipelines. Strong cross-functional leadership and communication skills, with a demonstrated history of influencing product managers, core software engineers, and external technical leaders without direct authority. Ways to stand out from the crowd: Direct experience with NVIDIA DSX, DGX systems, DGX Cloud, CUDA-X, or related NVIDIA deep learning system software stacks. Proven track record of open-source contributions or maintainer roles in the cloud-native or AI infrastructure space (e.g., Kubernetes, Kubeflow, Ray, vLLM, Triton). Deep understanding of enterprise AI cluster operations, multi-node GPU scaling,