Staff Machine Learning Engineer

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Company Overview

A leading global technology and delivery platform is expanding its subscription loyalty business through advanced personalization, artificial intelligence, and machine learning. Its DashPass subscription program provides members with lower delivery fees, faster estimated delivery times, third-party partnerships, discounts, promotions, and other benefits designed to maximize membership value.

The organization is investing heavily in personalization to efficiently grow its subscriber base while improving engagement and reducing subscriber churn. A newly forming team will leverage AI and advanced machine learning to power real-time decision-making across the subscriber journey, including personalized sign-up promotions, progressive reward systems, and pre-cancellation retention offers.

About the Role

The organization is seeking a Staff Machine Learning Engineer to lead the design and development of large-scale machine learning and optimization systems that power personalization initiatives throughout the subscriber journey.

This is a highly impactful individual contributor role for an experienced machine learning engineer who enjoys combining economic intuition, large-scale ML modeling, and system design to solve complex real-world optimization problems. The role will involve building and deploying innovative ML systems that directly influence subscriber growth, retention, spending efficiency, and overall marketplace health.

Key Responsibilities

• Contribute to causal inference modeling to measure the incremental impact of subscriber acquisition and retention strategies.
• Develop incentive optimization frameworks that personalize progressive rewards and improve spending efficiency.
• Build budget allocation and forecasting models to identify optimal spending across acquisition, referrals, and retention initiatives.
• Partner closely with Product, Data Science, and Engineering teams to design experiments, modeling frameworks, and production ML systems.
• Build and deploy 0-to-1 machine learning systems that improve subscriber outcomes and marketplace health.
• Provide technical mentorship and guidance to engineers and cross-functional partners while leading through influence rather than direct management.
• Establish and promote best practices for model training, evaluation, deployment, and monitoring.
• Drive complex technical initiatives from concept through production implementation.
• Apply advanced machine learning and optimization techniques to real-world personalization and subscription challenges.
• Help shape the technical direction of a new team with broad impact across multiple business problem areas.

Required Qualifications

• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related technical field.
• 8+ years of industry experience building and deploying production-scale machine learning systems.
• Strong understanding of probability theory, statistics, and machine learning fundamentals.
• Strong programming skills in Python, Java, or C++.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or XGBoost.
• 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.
• Proven ability to lead cross-functional initiatives and drive complex technical projects from beginning to end.
• Excellent communication skills with the ability to explain technical concepts to product, business, data science, and engineering audiences.
• Ability to provide technical leadership and mentorship while operating effectively as an individual contributor.

Preferred Qualifications

• Experience working with subscription growth systems.
• Experience with marketplace systems.
• Strong interest in building and helping lead a new team with broad organizational impact.
• Experience applying machine learning to personalization, optimization, causal inference, incentives, forecasting, or related business problems.
• Experience developing real-time machine learning and decision-making systems.

Disclaimer

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