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Software-Hardware Systems Engineer

2 weeks ago


California, Missouri, United States The Rundown AI, Inc. Full time
Software-Hardware Systems Engineer

We are a dynamic technology company dedicated to revolutionizing the field of artificial intelligence. Our team is comprised of talented engineers, researchers, and experts from diverse backgrounds, all united by a shared passion for innovation and collaboration.

We are seeking an experienced Software-Hardware Systems Engineer to join our team. As a key member of our engineering team, you will play a critical role in designing and developing optimized software and hardware solutions for our AI applications.

The ideal candidate will have a strong background in software-hardware co-design, experience working with machine learning algorithms, and a deep understanding of AI accelerators. You will collaborate closely with our cross-functional teams to understand the unique requirements of our AI workloads and design optimized software and hardware solutions to meet those needs.

Responsibilities:

  • Design and develop optimized software and hardware solutions for our AI applications.
  • Collaborate with our cross-functional teams to understand the unique requirements of our AI workloads.
  • Develop performance estimates for critical kernels for different hardware configurations and drive decisions on compute core and memory hierarchy features.
  • Build system performance models at different abstraction levels and carry out analysis to drive decisions on scale up, scale out, front-end networking.

Requirements:

  • 4+ years of industry experience, including experience harnessing compute at scale and optimizing ML platform code to run efficiently on target hardware.
  • Strong experience in software/hardware co-design.
  • Deep understanding of GPU and/or other AI accelerators.
  • Experience with CUDA, Triton or a related accelerator programming language.
  • Experience driving Machine Learning accuracy with low precision formats.
  • Experience with system performance modeling and analysis to optimize ML model deployment.