Machine Learning Engineer, Marketplace Optimization

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Company Overview

A leading global technology and delivery platform is expanding its advertising business across multiple verticals, including Grocery and Retail. Its Ads Marketplace uses artificial intelligence, machine learning, and advanced optimization techniques to improve advertiser performance, consumer experiences, and marketplace balance.

The Marketplace Optimization team is responsible for critical areas of the Ads Delivery funnel, including bidding, auction design, budget pacing, forecasting, targeting, and advertising experimentation. The team develops real-time machine learning systems that help optimize ad auctions, generate efficient bids, dynamically manage budgets, and improve the overall efficiency, fairness, and scalability of the advertising marketplace.

The organization is seeking a Machine Learning Engineer, Marketplace Optimization to design, build, optimize, and scale large-scale machine learning systems within the Ads Delivery funnel. The role combines applied machine learning, economic intuition, experimentation, and software engineering to solve complex real-world marketplace optimization problems.

The successful candidate will collaborate closely with Data Science, Product, Platform, Infrastructure, Marketing, Analytics, and Operations teams to develop innovative algorithms, evaluate their impact, and move machine learning solutions from prototypes into reliable production systems.

Key Responsibilities

• Design, build, and deploy machine learning models and pipelines for pacing, bidding, auction, and targeting optimization.
• Collaborate with Data Science and Product teams to develop and evaluate new algorithms through rigorous experimentation.
• Improve and scale existing machine learning infrastructure and data pipelines in partnership with Platform and Infrastructure teams.
• Write high-quality, maintainable code and participate in system design and peer reviews.
• Contribute to technical discussions and help shape the team’s engineering roadmap.
• Partner with Data Science and Marketing teams to design and execute lift tests.
• Collaborate with Platform teams on budget A/B testing and evaluation frameworks.
• Build and improve machine learning systems that have a direct impact on business performance and financial outcomes.
• Rapidly test hypotheses through robust sequential experiments and measure their impact on marketplace KPIs.
• Develop optimization pipelines using large-scale datasets while considering factors such as budget, fairness, and assignment rates.
• Partner with engineering, analytics, product, and operations teams to move models efficiently from prototype to production.
• Contribute to the continued development and growth of a rapidly expanding advertising platform.

Required Qualifications

• Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Machine Learning, Statistics, or a related field.
• Proficiency in using AI coding tools such as Claude Code, Codex, or Cursor throughout the software development lifecycle, including software design, code generation, testing, monitoring, and release.
• Industry experience building or maintaining machine learning systems in production.
• Strong understanding of machine learning fundamentals, statistics, and data modeling.
• Strong programming skills in Python, Java, or C++.
• Experience working with machine learning frameworks such as TensorFlow, PyTorch, or XGBoost.
• Excellent communication and collaboration skills with the ability to work effectively with Product, Data Science, and Engineering teams.
• Curiosity, a growth mindset, and a strong willingness to learn, iterate quickly, and take ownership of impactful projects.

Preferred Qualifications

• Familiarity with auction systems, bidding, forecasting, or budget optimization.
• Experience working in advertising, marketplaces, or other large-scale optimization environments.
• Familiarity with experimentation science and the design of lift tests.
• Experience with marketplace incrementality analysis.
• Experience working with large-scale datasets and real-time machine learning systems.
• Understanding of marketplace economics, optimization, and fairness considerations.

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