Senior Edge AI Engineer: Agentic GPU Deployment

Segment (Twilio)

Santa Clara (CA)

On-site

USD 152,000 - 287,500

Full time

14 days+

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Job summary

NVIDIA is seeking a seasoned engineer to lead local GPU deployment, profiling, and optimization efforts. You will collaborate with internal teams and external partners to optimize end-to-end AI workflows on NVIDIA GPUs, delivering high-performance results and guidance.

You will work with CUDA and Nsight, mentor junior engineers, and contribute to OSS projects while advancing GPU feature development. This role emphasizes hands-on problem solving in a fast-paced environment.

Qualifications

  • 5+ years of professional experience in local GPU deployment, profiling and optimization.
  • Bachelor's or Master's degree in Computer Science, Engineering, or related field.
  • Strong proficiency in C/C++, Python, and software design.
  • Experience with Windows and Linux; CUDA and Nsight.

Responsibilities

  • Work closely with internal engineering and product teams on solving GPU deployment challenges.
  • Apply profiling and debugging tools to optimize end‑to‑end AI workflows.
  • Develop sample code and host presentations to guide efficient AI deployment.
  • Improve OSS software performance, e.g., GGML, Llama.cpp, Ollama, vLLM, ONNX Runtime.
  • Collaborate with driver/architecture teams and NVIDIA research to influence GPU features.
  • Provide technical leadership and mentorship to junior engineers.

Skills

GPU deployment
Profiling & optimization
C/C++
Python
Software design
Windows & Linux
Communication skills

Education

Bachelor's or Master's degree in Computer Science, Engineering, or related field

Tools

CUDA
Nsight

Job description

NVIDIA is seeking a seasoned engineer to lead local GPU deployment, profiling, and optimization efforts. You will collaborate with internal teams and external partners to optimize end-to-end AI workflows on NVIDIA GPUs, delivering high-performance results and guidance.

You will work with CUDA and Nsight, mentor junior engineers, and contribute to OSS projects while advancing GPU feature development. This role emphasizes hands-on problem solving in a fast-paced environment.

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