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    Senior Machine Learning Engineer - Dallas, United States - Motion Recruitment Partners, LLC

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    Description

    We are a dynamic Supply Chain Logistics startup focused on enhancing visibility throughout the supply chain, offering innovative solutions to our clients by utilizing real-time data automation to pinpoint areas of improvement. We are currently seeking a Senior Machine Learning Engineer with 2-3 years of hands-on experience in developing End-To-End ML pipelines. The ideal candidate will have expertise in data extraction, proficiency in ML frameworks like PyTorch, Tensorflow, or Scikit-Learn, and a track record of optimizing ML models for performance and scalability. Strong familiarity with parallel computing, distributed systems, and accuracy tracking is a plus.

    Responsibilities:

    • Lead development of algorithms for structured data extraction from varied formats
    • Evaluate and choose tools for ML solutions in R&D
    • Design scalable ML systems for processing large datasets

    This is a fully remote position, with preference for candidates in the PST time zone due to the location of the hiring manager in San Francisco. In addition to a competitive salary, this role includes equity options, opportunities for professional growth, and a collaborative work environment with cutting-edge technology.

    Required Skills & Experience:

    • 2-3 years of Data Extraction experience
    • 2-3 years of End-To-End ML Pipeline Development
    • 1 year of Gen AI & LLMs experience
    • Proficiency in PyTorch, Tensorflow, or Scikit-Learn

    Desired Skills & Experience:

    • Background in Supply Chain Logistics
    • Experience in Computer Vision, Distributed Systems, Parallel Computing, Accuracy Tracking

    Benefits & Perks:

    • Equity Package
    • Fully Remote
    • Generous Paid-Time Off

    Applicants must be legally authorized to work full-time in the US. Join us in revolutionizing Supply Chain Logistics with cutting-edge technology as a Senior Machine Learning Engineer specializing in LLMs.



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