Machine Learning Engineer

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 clear 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 → scripts → jobs) 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 clear documentation. You’re known for thoughtful experimentation, practical solutions, and effective collaboration that helps the team deliver value faster.