Machine Learning Engineer (Contract-to-Hire)
Remote (U.S. – Eastern Time Hours Preferred)
An innovative software product organization is seeking a Machine Learning Engineer to support the design and deployment of models that power advertising personalization and smarter user segmentation. This individual contributor role will focus on building end-to-end ML pipelines, supporting cohort experimentation, and partnering cross-functionally with data, product, and marketing teams.
This is a contract-based opportunity with the potential to convert to a full-time position.
What You’ll Do:
Develop and deploy machine learning models that improve ad performance through personalization and audience targeting.
Design and maintain data pipelines to support scalable ML experimentation and cohort-based testing.
Leverage tools such as Google Ad Manager and other ad tech platforms to define, manage, and evaluate user cohorts.
Collaborate closely with cross-functional partners to design experiments, track campaign effectiveness, and refine targeting strategies.
Optimize model training workflows and feature engineering practices for advertising use cases.
Evaluate the business impact of ML solutions through metrics like CTR, CVR, and revenue per user.
Ensure model quality, reproducibility, and production readiness.
Contribute to a fast-moving environment where thoughtful experimentation meets pragmatic delivery.
What We’re Looking For:
3–5+ years of hands-on experience in machine learning, with a focus on advertising, martech, or audience targeting.
Familiarity with ad tech platforms such as Google Ad Manager (GAM), DV360, or similar tools.
Proven experience designing and evaluating user segmentation strategies in marketing or campaign settings.
Strong proficiency in Python and SQL; comfort with data wrangling, experimentation, and ML development frameworks.
Background in deploying models in real-time or large-scale production environments.
Knowledge of A/B testing, uplift modeling, or other experimentation and attribution techniques.
Ability to communicate clearly with both technical and non-technical stakeholders.
Degree in Computer Science, Statistics, Data Science, or a related field—or equivalent professional experience.