Senior Applied AI Engineer

caseguild.com

  • Seattle, Washington
  • 30+ days ago

    Highlights

    We’re a fast growing, early-stage Seattle startup building systems at the intersection of information retrieval, machine learning, and large language models , operating at billions of tokens where accuracy and speed matter. CaseGuild builds the industry’s most advanced evidence reasoning platform for complex litigation, helping legal teams investigate massive document sets and surface critical facts with speed and precision.

    Numbers & Facts

    LocationSeattle, Washington
    Websitecaseguild.com

    Description

    About CaseGuild


    CaseGuild builds the industry’s most advanced evidence reasoning platform for complex litigation, helping legal teams investigate massive document sets and surface critical facts with speed and precision.

    We’re a fast growing, early-stage Seattle startup building systems at the intersection of information retrieval, machine learning, and large language models, operating at billions of tokens where accuracy and speed matter. You’ll join a small, senior team that ships production systems daily, measures everything, and iterates based on real-world performance.

    The Role


    This role is for a startup engineer who is data-first, evaluation-driven, and has built production systems.

    You’ve spent years building ML or AI systems where success isn’t measured by demos, but by metrics, benchmarks, and real-world performance. You understand that modern LLM pipelines still require datasets, experiments, baselines, and failure analysis, and you enjoy owning that end-to-end.

    You Might Thrive in This Role If You:

    • Have 5+ years of experience building ML or applied AI systems where accuracy and evaluation mattered
    • Have designed and owned experiment frameworks and evaluation pipelines in production
    • Are fluent in metrics (precision, recall, F1) and know when each matters
    • Have strong foundations in classic ML, NLP, and information retrieval, now applied to LLM-based systems
    • Have experience working with multiple LLM providers and models, and don’t treat them as black boxes
    • Enjoy end-to-end ownership and pragmatic tradeoffs in a startup environment
    • Have a high sense of ownership and agency, with a bias toward getting 1% better every day
    • Care deeply about correctness, rigor, and repeatability

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