below marketmost similar roles pay hereabove market
This role pays less than
72%
of similar roles. Most pay
$168,662–$242,087
— the blue band above.
At the midpoint, this role pays about
$174k
versus about
$205k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Technical Marketing Engineer - AI Platform Software joins the AI Platform Software team to communicate advancements in the high-performance AI software stack to the developer community. This role involves investigating new training and inference features, writing technical blog posts, user guides, and reference examples, and building product-based training material. You will collaborate with product, engineering, and marketing teams to develop content for core kernel and communication libraries like cuDNN and NCCL, as well as platforms like PyTorch, Megatron, TensorRT-LLM, vLLM, and SGLang. Key responsibilities include benchmarking inference platforms, debugging multi-GPU jobs, and providing guidance to deep learning developers through code samples. The role requires proficiency in Python or C/C++ to solve technical problems regarding modern LLM architecture, quantization, and parallelism strategies.
Investigate new training and inference features to assess them from a developer's perspective.
Write technical blog posts, user guides, and reference examples for the developer community.
Create product-based training materials and how-to content with internal and external researchers.
Improve product documentation to ensure clarity for developers and their agents.
Build code samples and proof of concept applications to guide deep learning developers.
Benchmark and generate data to position NVIDIA's inference platforms as the lowest cost-per-watt.
Collect and relay community and customer feedback to product and engineering teams.
What we're looking for
Bachelor's degree in Computer Science, Computer Engineering, or a 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.
A portfolio of work written or built for a technical audience, such as a blog post, tutorial, documentation, or public repository.
Prior success in balancing multiple projects at a time.
Advanced knowledge of modern LLM and AI software architecture, including attention kernels, parallelism strategies, quantization, and KV cache management (preferred).
Experience running, tuning, or interpreting benchmarks on multi-GPU systems (preferred).
Generative AI
RAG
Agentic AI
LLM
Kubernetes
CI/CD
Git
NVIDIA AI Enterprise
NIM
NeMo
TensorRT
Triton Inference Server
RAPIDS
CUDA
Helm
APIs
SDKs
Cloud-native
Docs-as-code