NVIDIA
Senior AI Developer Technology Engineer
$184K–$356K
US, CA, Santa Clara +5 · Senior · Full-time · Posted Oct 29, 2026
- Location
- US, CA, Santa Clara
US, MA, Westford
US, TX, Austin
US, OR, Hillsboro
US, NC, Durham
US, CA, Remote - Experience
- 8+ years of relevant work or research experience in software engineering and performance tuning.
- Education
- MS in Computer Science, Computer Engineering, or related computational field (or equivalent experience).
About the job
Accelerates generative AI and other complex workloads on NVIDIA systems by optimizing algorithms and software, collaborating with developers, and informing future hardware and software design.
What you'll do
- Develop GPU-acceleration techniques and optimize generative AI training and inference, algorithms, frameworks, libraries, and open-source projects.
- Partner with developers and technical experts to analyze and optimize AI algorithms on CPU and GPU architectures.
- Collaborate with internal teams on next-generation hardware and software, and share findings through developer blogs and industry conferences.
What they ask for
- MS in Computer Science, Computer Engineering, or related computational field (or equivalent experience).
- 8+ years of relevant work or research experience in software engineering and performance tuning.
- Programming fluency in C/C++; deep understanding of algorithms and software development.
- Background in accelerated computing, parallel programming, performance analysis and optimization, low-level performance optimization, and modern CPU and GPU architectures.
Nice to have
- Expertise optimizing LLMs, Diffusion Models, and Multimodal Architectures for training and inference; experience with training and inference stacks, serving frameworks, and pre-training and post-training pipelines.
- Strong foundation in linear algebra and numerical methods.
Pay
184,000 USD - 287,500 USD (base salary, Level 4); 224,000 USD - 356,500 USD (base salary, Level 5)
Tools
C/C++, LLMs, Diffusion Models, Multimodal Architectures, CPU, GPU
AI focus
Accelerates generative AI training and inference, including LLMs, diffusion models, and multimodal systems; optimizes algorithms and contributes to AI frameworks, libraries, and pipelines.
Model-written summary · the original posting is authoritative