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    Machine Learning Engineer - Los Angeles, United States - Acceler8 Talent

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    Description
    Machine Learning Engineer (LLMs, Training, Optimization, Inference)


    We are looking for a skilled Machine Learning Engineer with a strong systems focus to join our team as an Individual Contributor.

    This role is perfect for a dedicated professional who excels in dynamic environments, applying deep technical expertise in machine learning and systems integration to dramatically improve computational performance and efficiency.


    Our company is actively developing an advanced integrated chip and software stack designed for high-level computing applications, with the future potential to support artificial general intelligence (AGI).

    Our objective is to create a platform that significantly exceeds current industry standards in cost-effectiveness and performance, establishing new benchmarks for computational efficiency.


    As an Individual Contributor in Machine Learning Engineering, you will take on crucial tasks of training and optimizing large language models (LLMs) for our innovative hardware.

    Your focus will include engineering distributed systems for efficient training and inference, ensuring our technology delivers unmatched performance-per-dollar, far surpassing typical industry advancements.


    What We Offer:
    Competitive salary and meaningful equity in an industry-leading company.
    A benefits package designed to fit your life and needs.
    A dynamic work environment that fosters innovation, collaboration, and continuous learning.
    Opportunities for professional growth in a rapidly evolving technological landscape.


    Key Responsibilities:
    Tailor and optimize LLMs to exploit the full capabilities of our specialized hardware effectively.
    Conduct comprehensive quality evaluations to ensure our systems meet the highest performance standards.
    Architect and maintain scalable distributed systems that enhance training and inference processes.
    Provide insights and recommendations on hardware architecture to maximize the efficiency and effectiveness of machine learning applications.

    Relevant Keywords:
    Machine Learning, Large Language Models, Advanced Computing, Systems Integration, Distributed Infrastructure, Hardware Optimization, Neural Network Training.

    #J-18808-Ljbffr


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