Machine Learning Engineer

Department Description

The Data and AI team sit within Palmetto’s Product, Design, and Engineering function and are responsible for the strategy, infrastructure, and rigor of data across the company. We build reporting, models, and products that drive impact through our business, operational, and engineering teams. 

Location

This position will be based in Mexico City.

Reporting

This position will report to the VP of Data and AI

Summary of Role 

Palmetto is seeking a Machine Learning Engineer. As one of Palmetto’s first two Machine Learning Engineers, you will be responsible for end-to-end model development for core business problems, from feature engineering and experimentation to production deployment and performance monitoring. You will use and contribute to our nascent MLOps platform to rapidly iterate and deliver high-impact predictive insights that contribute directly to Palmetto’s mission. 

Strategic & Tactical

• Design, train, and rigorously evaluate high-impact machine learning models across various business domains
• Contribute to the creation and management of the core Machine Learning Platform, and help the broader data community transition existing models and analysis to this new infrastructure
• Collaborate with Data Scientists and Data Engineers to develop, validate, and productionize robust feature pipelines for model training and inference. Build and evangelize practices to support on-going experimentation, validation, and performance analysis.
• Implement and iterate on various modeling techniques to address business goals creatively and efficiently.
• Build tools for monitoring model drift, performance, and bias into the MLOps platform
• Write clean, well-tested, and maintainable Python code for models and surrounding deployment logic. 

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related quantitative field (or equivalent experience).
• 2-4 years of experience in machine learning or data science, focusing on building and deploying models.
• Deep expertise in Python and proficiency in SQL
• Strong practice experience with statistical modeling toolskits such as PyTorch, XGBoost, Scikit-learn, TensorFlow, or VertexAI.
• Familiarity with containerization and CI/CD concepts; comfort using MLOps platform.
• Proven ability to understand business problems and think creatively about how machine learning might help solve them.
• Insatiable curiosity and love of learning.

Success Defined

• We successfully launch multiple high-impact ML models into production within the first 9 months
• Help refine the MLOps platform as the primary customer
• Create self-sustaining governance practices for model documentation, monitoring, and governance

Employment is contingent upon the successful completion of a background check.