Staff SW Systems Engineer (9291)

6 Months ago • All levels

Job Summary

Job Description

Seeking a Senior/Staff/Principal Engineer with deep expertise in GPU/TPU acceleration for Edge AI. Must have extensive hands-on experience in local Large Language Model (LLM) inference and embedded GPU/TPU architectures. You'll play a crucial role in shaping future Edge AI solutions by developing and optimizing AI inference models for deployment on edge devices.
Must have:
  • GPU/TPU Acceleration
  • Edge AI Inference
  • Large Language Models
  • Embedded Architectures
Good to have:
  • Micro-architecture Development
  • Performance Profiling
  • Edge Computing Platforms
  • AI Frameworks

Job Details

Senior/Staff/Principal Engineer – Edge AI LLM 9291

We are seeking a talented Senior/Staff/Principal Engineer with specialized expertise in GPU/TPU acceleration to join our team. The ideal candidate will have extensive hands-on experience in local Large Language Models (LLM) inference with embedded GPU/TPU architectures. As Principal Engineer specializing in Edge AI, you will play a crucial role in shaping the future Edge AI solution, leveraging the power of GPU/TPU acceleration and enterprise grade, large scale edge compute.
 
The successful candidate will combine technical excellence with effective leadership, creating a positive impact on both projects and team dynamics.

Key Responsibilities:

    • High-Level Design and Architecture
    • Influence the Edge AI strategy by providing expert advice on design and architecture.
    • Make critical decisions regarding technical directions, scalability, and system performance.
    • Develop and optimize AI inference models for deployment on edge devices with embedded GPU/TPU accelerators, focusing on local Low Latency Model (LLM) inference.
    • Implement and fine-tune low-latency model inference pipelines to meet real-time performance requirements.
    • Collaborate with cross-functional teams to integrate AI inference solutions into edge computing platforms and applications.
    • Collaborate with the GPU Hardware Design Team to design and optimize GPUs that power next-generation devices.
    • Conduct performance profiling and optimization to maximize the efficiency of GPU/TPU acceleration for local LLM inference.
    • Work on micro-architecture development, ensuring efficient execution of graphics, compute, and AI workloads within energy and area constraints.
    • Stay current with advancements in GPU/TPU technologies and edge AI frameworks, incorporating them into solution designs as appropriate.
    • Provide technical expertise and support to project teams, ensuring successful implementation and deployment of edge AI solutions.

Team Leadership:

    • Lead and inspire a team of engineers, providing guidance, setting goals, and ensuring collaboration.
    • Oversee project planning, execution, and delivery, ensuring alignment with business objectives.
    • Manage all phases of technical projects, from conception to completion.
    • Develop project specifications, track progress, and control costs.
    • Foster a positive work environment, encouraging professional growth and knowledge sharing.
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