Machine Learning Engineer

Employment Type: [Not Specified] Location Type: [Not Specified] Salary: [Not Specified]

Position Overview

At MyFitnessPal, we believe good health starts with what you eat. We provide tools, resources, and support to enable users to reach their health goals. Our users rely on MyFitnessPal to power their health and fitness journeys every day. As a member of our MyFitnessPal Data Science team, you’ll have the opportunity to positively impact those users by creating the predictive models and AI that drives the MyFitnessPal ecosystem. In addition to technical expertise and passion, you’ll find that your teammates value collaboration, mentorship, and inclusive environments.

Essential Duties

The Machine Learning Engineer will play a key role in designing and deploying models that improve advertising performance and enable smarter user segmentation. You will be responsible for the technical implementation of ML pipelines that support ad personalization, targeting strategies, and cohort experimentation. To thrive in this role, you’ll need to be comfortable working cross-functionally with marketing, product, and data stakeholders, using tools like Google Ad Manager (GAM) to define and evaluate user cohorts. You should be able to iterate quickly, make pragmatic decisions, and balance experimental rigor with product delivery. You know that success in this role requires strong communication, thoughtful experimentation, and a data-driven mindset.

This is an individual contributor role with strong mentorship expectations.

MyFitnessPal encourages innovation and adoption of the latest technologies available to deliver an amazing experience for our members. Our diverse team of brilliant technologists builds and maintains native mobile applications, a web application, the world’s largest nutrition database, constantly evolving data science and AI/ML assets, the backend infrastructure and data platform required to support these applications and databases, as well as the business systems and data required to manage an awesome company.

Technologies, services and/or languages we work with: MySQL, Snowflake, Databricks, Elasticsearch, Python, SQL, Kubernetes, Docker, Scale.ai, AWS Ground Truth, Doccano.

If you are highly motivated, results-oriented, and thrive in a fast-paced and collaborative environment, please apply. We’re looking to add talent that can help further define the culture we’re creating: consumer-centric, curious, and always innovating.

What you’ll be doing:

• Build and deploy machine learning models and data pipelines to power advertising personalization, user segmentation, and audience cohorting.
• Leverage GAM and other ad tech tools to create and evaluate user cohorts for targeting and experimentation.
• Partner with marketing, data science, product, and engineering teams to define metrics and evaluate model-driven campaigns.
• Design experiments to measure model impact on advertising performance (e.g., CTR, CVR, revenue per user).
• Build pipelines and systems to support scalable cohort-based testing and ML deployment.
• Optimize feature engineering, model training, and evaluation loops for advertising-related use cases.
• Collaborate with stakeholders to identify opportunities for improving user targeting and increasing campaign ROI.
• Ensure models meet high standards for performance, reliability, and reproducibility.

Qualifications to be successful in this role:

• Have 4–6+ years of machine learning experience, ideally within advertising, martech, or audience targeting domains.
• Hold a Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field—or equivalent experience.
• Have hands-on experience with ad tech platforms such as Google Ad Manager (GAM), DV360, or similar tools.
• Demonstrate the ability to build, test, and iterate on user segmentation strategies for marketing or advertising.
• Are comfortable working with cross-functional teams to define user cohorts and evaluate their performance.
• Are highly proficient in Python and SQL, with experience in data wrangling.