Emerson Ramey

Software Engineer at KLA

Michigan, United States

About

I am a recent graduate from the University of Michigan where I earned both a Bachelor of Science in Engineering and a Master of Science in Engineering in Computer Science. Here, I acquired proficiency in C++ and Python. Currently, I am leveraging my skills and knowledge as a Software Engineer at KLA in Ann Arbor. I am fortunate to be part of an exceptional team where I can continuously challenge myself and collaborate with others on complex engineering problems.

Experience

  • KLA ()
    • Software Engineer
      Jun 2025 - Present · 1 yr 2 mos

    • Software Engineer
      Aug 2024 - Jun 2025 · 11 mos

    • Software Engineer
      Jul 2024 - Aug 2024 · 2 mos

  • Autonomous Navigation Lead at Michigan Mars Rover - Student Project Team
    Jun 2023 - Jun 2024 · 1 yr 1 mo

  • Software Engineering Intern at KLA
    May 2023 - Aug 2023 · 4 mos

    • Designed four easy-to-use, stand-alone APIs to store and retrieve image data of varied formats, abstracting the knowledge of how the data is saved from the user • Implemented a library in C++ around these APIs to allow users to retrieve stored image data through a variety of applications in 0.001% of the time it takes the current implementation

  • Summer Undergraduate Researcher in Engineering (SURE) in the Interactive Sensing and Computing Lab at University of Michigan College of Engineering
    May 2022 - Aug 2022 · 4 mos

    • Collaborated with professors, PhD students, undergraduates, and the University’s Medical team in the Interactive Sensing and Computing Lab to create a privacy microphone system that tracks hundreds of patients’ daily activities, a project called PrivacyMic • Used Python and Dropbox API to implement a robust program that continuously runs for seven days and automatically detects and uploads over 300,000 newly created audio files to Dropbox to be stored for later use • Created a sound event detection system in Python with a Raspberry Pi that saves audio files of sounds in real time then optimized the algorithm to improve detection speed by 99% and accuracy by 50%