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    Machine Learning Engineer: Recommendations (Remote)

    Constructor
    PortugalTELECOMMUTE
    Remote
    Senior
    Full Time
    about 6 hours ago
    💰$80,000 - $120,000
    Machine LearningRecommendationsRemotePythonSQLBig DataA/B Testing

    Requirements

    • Deep understanding of ML fundamentals and experience building large-scale recommendation, retrieval, or ranking systems
    • Expertise in Python and SQL, with hands-on experience in big data systems (e.g., Spark, Presto/Athena, Hive)
    • Production-level ML experience, including deploying models to production and designing A/B tests to validate business impact
    • Analytical mindset with the ability to translate intuition into data-driven hypotheses and high-value engineering solutions
    • Excellent communication skills in English, capable of explaining complex technical concepts to non-technical stakeholders

    What You'll Do

    • Build high-load, real-time recommendation service
    • Build end-to-end ML pipelines driving measurable business impact
    • Design metrics to evaluate recommendation relevance and performance
    • Lead the full development lifecycle from initial design to production
    • Participate in strategic planning to help drive product evolution and prioritization
    • Collaborate with stakeholders to align technical roadmaps with business needs

    Benefits

    • Unlimited vacation time
    • Fully remote team - choose where you live
    • Work from home stipend
    • Apple laptops provided for new employees
    • Training and development budget refreshed each year
    • Maternity & Paternity leave for qualified employees
    • Work with smart people who will help you grow and make a meaningful impact
    • Base salary: $80k–$120k USD, depending on knowledge, skills, experience, and interview results
    • Stock options offered in addition to the base salary
    • Regular team offsites to connect and collaborate

    About Constructor

    Constructor is an AI-enabled product search and discovery platform that provides assistance to e-commerce brands.

    San Francisco, CA, US
    100 - 250
    eCommerce