Overview The Machine Learning Engineer will design build and productionize ML solutions that improve business outcomes You ll contribute to end-to-end model development collaborate with cross-functional teams and ship reliable well-tested models and services that make a measurable impact Prodege A cutting-edge marketing and consumer insights platform Prodege has charted a course of innovation in the evolving technology landscape by helping leading brands marketers and agencies uncover the answers to their business questions acquire new customers increase revenue and drive brand loyalty product adoption Bolstered by a major investment by Great Hill Partners in Q4 2021 and strategic acquisitions of Pollfish BitBurst AdGate Media in 2022 Prodege looks forward to more growth and innovation to empower our partners to gather meaningful rich insights and better market to their target audiences As an organization we go the extra mile to Create Rewarding Moments every day for our partners consumers and team Come join us today Primary Objectives Applied ML Model Development Evaluation Data Preparation Feature Engineering Experimentation Productionization of Models with Monitoring Iteration Cross-Functional Collaboration with Data Product Engineering Code Quality Testing and Documentation Performance Tuning and Practical Problem Solving 80 20 focus Qualifications - To perform this job successfully an individual must be able to perform each job duty satisfactorily The requirements listed below are representative of the knowledge skill and or ability required Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions Detailed Job Duties Implement ML models classification regression ranking NLP or recommendation using Python and common ML libraries e g scikit-learn XGBoost familiarity with PyTorch or TensorFlow Prepare and analyze datasets build features conduct exploratory analysis and design experiments with explicit success metrics Train validate and compare models document methodology trade-offs and results select approaches grounded in business goals Package models for deployment APIs batch jobs or streaming in partnership with software data engineering add basic telemetry for performance and drift Write clean maintainable and testable code participate in code reviews and follow version control and branching standards Monitor and iterate on model performance in production troubleshoot issues and implement improvements Collaborate with stakeholders to translate requirements into measurable ML deliverables communicate progress risks and findings Contribute to lightweight automation notebooks scriptsjobs and reusable utilities that improve team velocity What does SUCCESS look like Success means shipping reliable ML features to production that measurably improve KPIs e g accuracy latency revenue lift fraud reduction while maintaining code quality and explicit documentation You re known for thoughtful experimentation practical solutions and effective collaboration that helps the team deliver value faster The MUST Haves Bachelor s degree in Computer Science Engineering Mathematics or related field or equivalent practical experience Three or more 3 years of hands-on experience applying machine learning in production settings Strong proficiency in Python solid software engineering fundamentals testing modular design version control familiarity with SQL Practical experience with core ML techniques and algorithms e g tree-based models linear logistic regression clustering and model evaluation Working knowledge of one deep learning framework PyTorch or TensorFlow and when to use it vs classical ML Experience building data pipelines or jobs to train score models familiarity with Spark or similar is a plus Exposure to deploying models batch or real-time and monitoring basic health performance metrics Clear written and verbal communication skills ability to partner with product engineering and analytics Strong analytical problem-solving skills and a bias toward practical timely solutions 80 20 The Nice to Haves Master s degree AI Machine Learning or related fields is a plus Experience with cloud ML tooling AWS GCP or Azure MLflow model registries or basic MLOps practices Working knowledge of APIs and microservices concepts comfort containerizing workloads Docker Familiarity with NLP LLM tooling e g Hugging Face embeddings retrieval and prompt or fine-tuning workflows Advanced degree in a quantitative field or relevant certifications cloud ML