About the job
NVIDIA is looking for a Technical Marketing Engineer (TME) to communicate the latest advancements in our AI Platform Software to the developer and user community. The AI Platform Software team manages the complete high-performance AI software stack: core kernel and communication libraries such as cuDNN and NCCL, open training platforms like PyTorch, and Megatron, and accelerated inference platforms such as TensorRT-LLM and Dynamo. This role works closely with product, engineering, and marketing teams to develop key technical content that guides developers how to use NVIDIA's AI platforms.
Responsibilities
Investigating new training and inference features while assessing them from a developer's point of view. Writing blog posts, guides, and reference examples that developers can use.
Collaborating with internal and external deep learning engineers and researchers to build product-based training material and how-to technical content.
Being the champion for AI among NVIDIA developers by directly engaging with our developer community.
Improving product documentation to be clear for developers and their agents.
Growing the value of our software by bringing community and customer feedback back to our product and engineering teams.
Providing guidance to deep learning developers by building code samples and proof of concept applications.
Benchmarking and generating data for positioning NVIDIA's inference platforms as the lowest cost-per-watt.
Qualifications
Minimum
Bachelor's degree in Computer Science, Computer Engineering, or similar field or equivalent experience.
4+ years of practical experience in deep learning or machine learning, including research conducted during undergraduate and graduate studies.
Hands-on experience with at least one training or inference framework such as PyTorch, JAX, Megatron, TensorRT-LLM, vLLM, SGLang, or comparable tools.
Solid understanding of Python or C/C++, programming techniques, and software development.
Something you have written or built for a technical audience that we can engage with: a blog post, tutorial, documentation set, conference talk, thesis chapter, or public repository.
Passion for presenting to technical audiences and crafting content for developers.
Prior success in balancing multiple projects at a time.
Preferred
Advanced knowledge of modern LLM and AI software architecture: attention kernels, parallelism strategies, quantization, KV cache management, and request scheduling.
Sustained contributions to publicly accessible AI projects or developer forums.
Experience running, tuning, or interpreting benchmarks on multi-GPU systems.
Experience explaining a system you did not build to people who need to use it tomorrow.