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    Founding Full-Stack Engineer In Office - San Francisco, United States - Resemble

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    Description


    Global retail represents $30 trillion USD in revenue, but the revenue tools available to manage retail revenue decisions are antiquated.

    Operators in retail organizations make ad hoc pricing decisions in spreadsheets, without sufficient measurement or accountability.

    At Luca, we are on a mission to bring world class price optimization solutions to retail, powered by machine learning and delivered via an intuitive, data rich and configurable dashboard.

    We were founded in January 2023, and are backed by Y Combinator and Menlo Ventures. We are a small team of engineers and data scientists, and serve mid to large-size enterprise retailers.

    Today we serve several grocery, apparel, CPG and furniture retailers who have deeply entrenched Luca into their daily pricing and promotional processes.

    Our customers rave about Luca's easy-to-use UI and ML-powered insights.

    We are now looking for the engineer who helps us take these early signals of PMF and helps us make it repeatable and scalable.

    The Role
    We are looking for a Founding Full-Stack Engineer to join our small, but scrappy team in San Francisco.

    This role is for you if you are a motivated and hard-working full-stack engineer excited to be one of the first hires at an early-stage startup.

    Our Technical Stack

    Frontend:
    TypeScript, React, RadixUI, Tailwind


    Data Science:
    Python

    Qualifications

    5+ years of full-stack development experience
    Ability to own projects end-to-end including scoping, system design, implementation, testing, QA, deployment, and maintenance
    Strong communication and collaboration skills
    Expertise in Typescript and React
    Excited to working in-person in San Francisco
    Bonus Qualifications

    Familiarity or experience with AWS and basic infra/devops
    Familiarity or experience with Python and data science libraries like Pandas
    Familiarity or experience with data engineering, data pipelines, ETL, etc.

    #J-18808-Ljbffr

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