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Senior Developer Technology Engineer - Edge Agentic AI

Published · Updated

$152,000–$287,500annualizedSanta Clara, CASenior

Enables and optimizes local, end-to-end agentic AI deployment on NVIDIA RTX and DGX platforms, working with internal teams, external developers, and enterprise ISVs.

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View posting at nvidia (eightfold)

At a glance

Posted base pay

$152K–$288KAbove the median

The work

Builds what the customer runs

Balance

  1. Mostly building
  2. Mixed
  3. Mostly pre-sales or support

Project stages

  1. Pre-sales (not covered)
  2. Proof of concept (not covered)
  3. Implementation
  4. Operations

Travel Required, amount not stated

Coding
Writes production code
Builds for
External customers
Level
Senior
Role
Technical lead

What they ask for

Experience

  • 5+ yearsProfessional experience in local GPU deployment, profiling, and optimization

Tools

  • CUDA
  • NVIDIA Nsight
  • GGML
  • Llama.cpp
  • Ollama
  • vLLM
  • ONNX Runtime
  • TensorRT-RTX
  • cuDNN
  • NVIDIA Model Optimizer

Less common in FDE jobs

  • Individual contributor or manager: Technical lead47%

Share of US FDE postings that state each fact.

The job description from nvidia (eightfold)

At NVIDIA, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

As a Developer Technology Engineer, you will be at the forefront of innovation, working with leading industry partners and pioneering open-source projects to enable professional agentic AI workflows at the edge powered by NVIDIAs RTX and DGX platforms. This role offers an outstanding opportunity to collaborate with world-class talent and make a significant contribution to the evolving landscape of enterprise and consumer agentic AI.

What you'll be doing

  • Work closely across internal engineering and product teams as well as external app developers and enterprise ISVs on solving local end-to-end agentic AI GPU deployment challenges on NVIDIA RTX & DGX.
  • Apply powerful profiling and debugging tools for analyzing most demanding accelerated end-to-end agentic AI workflows to detect insufficient system utilization resulting in suboptimal runtime performance.
  • Conduct hands-on trainings, develop sample code and host presentations to give good guidance on efficient end-to-end agentic AI deployment targeting optimal runtime performance.
  • Improve LLM & GenAI user experience by working on feature and performance enhancements of OSS software, including but not limited to projects like GGML, Llama.cpp, Ollama, vLLM, ONNX Runtime.
  • Collaborate with GPU driver and architecture teams as well as NVIDIA research to influence next generation GPU features by providing real-world workflows and giving feedback on partner and customer needs.
  • Providing technical leadership and mentorship to junior engineers, encouraging an inclusive and high-performing team environment.

What we need to see

  • A proven track record 5+ years of professional experience in local GPU deployment, profiling and optimization.
  • A Bachelor's or Master's degree or equivalent experience in Computer Science, Engineering, or a related field.
  • Strong proficiency in C/C++, Python, software design, programming techniques..
  • Familiarity with and development experience on Windows and Linux.
  • Experience with CUDA and NVIDIA's Nsight GPU profiling and debugging suite.
  • Some travel is required for conferences and for on-site visits with external partners.
  • Strong problem-solving skills and the ability to work both independently and collaboratively in a fast-paced environment.
  • Excellent interpersonal and communication skills and a passion for keeping track with the latest advancements in AI technology.

Ways to stand out from the crowd

  • Experience with GPU-accelerated AI inference driven by NVIDIA APIs and SDKs, specifically TensorRT-RTX, cuDNN, NVIDIA Model Optimizer.
  • Expertise with professional agentic AI use cases, i.e., digital content creation and productivity workflows.
  • Experience working with open-source LLM and GenAI software.
  • Detailed knowledge of the latest generation GPU architectures.
  • Experience with AI deployment on NPUs and ARM architectures.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 26, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

View posting at nvidia (eightfold)

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Pay and conditions

Posted pay
$152,000–$287,500Annualized in US dollars
Location
Santa Clara, CA
Employment type
Full-time
Travel
Travel required, amount not stated
Experience requirements
  • 5+ years: Professional experience in local GPU deployment, profiling, and optimization (required)