Lead AI/LLM Network Engineer

4 days ago


San Jose, United States Collabera Full time

Title: Lead AI/LLM Network Engineer

Location: San Jose, CA - Hybrid, in office 2 days/remote 3 days

Full-Time Direct Hire W2

Salary: $200K - $220K


About the Role:

Our client is seeking a Senior/Tech Lead AI/LLM Network Software Development Engineer to join their cutting-edge team in San Jose, CA. In this role, you will design and implement high-speed network technologies and infrastructure to support large-scale AI/LLM (Artificial Intelligence/Language Learning Model) applications. You will play a crucial role in developing monitoring, and diagnostic platforms, and optimizing host-network-application interactions to enhance the scalability, reliability, and performance of AI/LLM networks.


Day-to-Day Responsibilities:

  • Design, implement, and deploy high-speed network technologies to support AI/LLM applications.
  • Develop platforms and systems for monitoring, analyzing, and diagnosing large-scale AI/LLM networks.
  • Research and develop high-performance AI communication frameworks, and network protocol stacks, and optimize the interaction between host, network, and application to improve scalability, reliability, and performance.
  • Build next-generation AI network infrastructure to support large-scale heterogeneous network hardware with innovative, deployable solutions.


Basic Qualifications:

  • Bachelor’s degree or higher in Computer Science, Electronic Engineering, Network Engineering, or a related field.
  • Proficiency in computer networks and network programming.
  • Strong programming skills in one or more mainstream programming languages (e.g., C/C++, Python, Go).
  • Familiarity with the latest advances in high-speed network systems, including RDMA, congestion control, and AI network optimization.


Preferred Qualifications:

  • Experience in developing high-performance communication frameworks (e.g., NCCL, MPI, RPC libraries).
  • Experience in developing software systems for AI network diagnosis and performance optimization.


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