X3M
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    Engineering

    ML Engineer

    Full-timeRemote

    Role Description

    This is a full-time role for a Semi-Senior or Senior Machine Learning Engineer on X3M’s AI/ML team. We are building the intelligence layer of our mediation platform. The role focuses primarily on machine learning: models that predict user and ad-market behavior and act on those predictions in real time. You will also collaborate on our generative AI work, such as the AdOps Agent, an LLM agent that helps our AdOps team and our customers run their monetization. The Machine Learning Engineer will be responsible for tasks such as:

    • Building and shipping predictive models, such as user churn, lifetime value and payer propensity, from exploration on Databricks to real-time serving in our ad mediation stack.
    • Developing and maintaining production ML pipelines using Airflow, Spark/Databricks, and AWS, covering training, validation gates and model promotion.
    • Monitoring models in production for calibration, drift and business impact, and measuring that impact through A/B tests.
    • Collaborating on generative AI initiatives such as the AdOps Agent, including tool integrations over our analytics and configuration APIs, evaluation sets, observability and human-in-the-loop controls.
    • Contributing to AI-driven software development by building the tooling and context that let coding agents work effectively across our codebase.
    • Collaborating with cross-functional teams (Data Engineering, Backend, SDK, AdOps and Product) to turn models into product features.

    This role is located in Buenos Aires with flexibility for remote work.

    Qualifications

    • Proven experience (3+ years) as a Machine Learning Engineer, Data Scientist or in a similar role, including taking models to production and maintaining them there.
    • Proficiency in Python and SQL, including the scientific Python stack (pandas or Polars, scikit-learn).
    • Experience with a range of model types, such as classification, regression and ranking models, and the judgment to choose the right one for the problem.
    • A solid grounding in statistics, including probability calibration, imbalanced classification, evaluation metrics beyond accuracy, and A/B testing.
    • Hands-on experience building with LLMs, including tool calling, prompt design and evaluating agent output, and using coding agents in your day-to-day development.
    • Familiarity with data platforms, particularly Spark/Databricks, Airflow and AWS.
    • Problem-solving skills and the ability to troubleshoot models and data in production.
    • A strong learning drive and ownership of outcomes: you pick up new domains and tools quickly, and you keep going until the goal is reached.

    For the Senior level, we also expect 5+ years of experience and a track record of owning initiatives end to end, from framing the problem to measured impact in production, as well as guiding technical decisions and mentoring other engineers.

    Nice to have

    • Background in AdTech, mobile games or app monetization.
    • Exposure to basic reinforcement learning techniques, such as multi-armed and contextual bandits.
    • Experience with agent frameworks such as Pydantic AI or LangGraph, or with the Model Context Protocol (MCP).