Machine Learning Engineer, Growth

About the role

The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy - a future where mundane repetition disappears and being known unlocks access, comfort, and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn't coming; it's here, and we need builders, innovators, and problem solvers to help us create it.

Responsibilities

• Design, develop, and productionize demand forecasting models optimized for different business goals (e.g., visits, revenue, availability)
• Innovate and improve Machine Learning models for price elasticity, time series, and probabilistic models for revenue optimization
• Design and build end-to-end data pipelines to support large-scale production usage
• Identify data issues (e.g., bias, leakage, labeling inconsistencies) and drive solutions
• Design and analyze experiments (A/B, switchback, causal inference) to validate pricing strategies
• Deploy and monitor models in production, ensuring reliability, scalability, and data quality
• Collaborate with product, engineering, and business teams to translate requirements into scalable ML solutions

Requirements

• PhD in Computer Science, Statistics, Economics, Applied Mathematics, or a related STEM field, with at least 1+ years of relevant experience, or MS with equivalent publications
• Proficient programming skills in Python and SQL
• Foundational experience in machine learning modeling and statistics, such as time series forecasting, probabilistic models, and deep learning models
• Strong knowledge with forecasting, optimization, and decision-making algorithms, including revenue maximization, constrained optimization, and demand/price curve optimization
• Solid understanding of causal inference and experimentation, with experience evaluating both short-term and long-term effects (A/B testing, DiD, uplift modeling)
• Hands-on experience with data pipeline development, including AWS data storage, data transformation, distributed processing (Spark), and workflow orchestration (Airflow)
• Strong communication skills, both written and verbal, with the ability to operate effectively at team and deep technical levels
• Comfortable reading academic papers and formulating concepts using mathematical notation
• Metropolis may utilize an automated employment decision tool (AEDT) to assess or evaluate your candidacy for employment or promotion. AEDTs are used to assist in assessing a candidate's application relative to the required job qualifications and responsibilities listed in the job posting.
• As part of this process, Metropolis retains data relevant to your candidacy, including personal information, for a period that is reasonably necessary for the use o

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