Machine Learning Engineer

We are seeking a Machine Learning Engineer who brings the analytical rigor of a data scientist and the engineering discipline of a software architect. In support of Colgate-Palmolive’s purpose to Make More Smiles and our commitment to a healthier future for our people, pets, and planet, this role builds the advanced machine learning capabilities that power smarter decisions, accelerate innovation, and create measurable impact across our global enterprise.

What You Will Do

Productionize ML Research: Transition experimental models into robust, scalable production services. You don't just build the model; you build the pipeline that sustains it. Pipeline Orchestration: Design and maintain complex data and ML pipelines using Airflow and dbt to ensure data integrity and model reliability. Statistical Rigor: Apply advanced statistical modeling and hypothesis testing to validate models, ensuring outcomes are testable and honest.

Why It Might Be a Fit

This role requires a strong analytical background, experience with machine learning engineering, and a passion for innovation and problem-solving. The ideal candidate will be able to work independently and collaboratively, with a strong focus on delivering high-quality results and driving business outcomes.

Requirements

• Bachelor’s Degree (or higher) in a high-rigor field: Statistics, Physics, Chemistry, Mathematics, Data Science, or Computer Science with a heavy emphasis on Statistical Learning.
• 6+ years of technical experience; Masters or PhD (3+ years)
• Proven expertise in Data Science and/or Machine Learning Engineering.
• Advanced proficiency in Python (Production-grade) and SQL.
• Hands-on experience with Airflow for orchestration and dbt for transformation.
• Familiarity with modern IDEs and Agentic Coding systems (e.g., Cursor, Windsurf, Claude Code, Antigravity) to maximize output velocity.
• Modern Stack: Expert knowledge of Python, Scikit-learn, major ML Libraries
• Data Engineering: Deep understanding of data lifecycle (ETL/ELT), data architecture, best practices for templatized data transformation
• Engineering Excellence: Familiar with Docker/Kubernetes, CI/CD, Git, and 'Software Engineering for ML' best practices.
• LLM Literacy: Familiar with concepts underpinning LLMs, and strategies to integrate GenAI into MLE project lifecycle

Benefits

• Salary Range $130,000.00 - $170,000.00 USD
• Discretionary bonuses, profit-sharing, and long-term incentives for Executive-level roles.
• Comprehensive benefits package, including medical, dental, vision, basic life insurance, paid parental leave, disability coverage, and participation in the 401(k) retirement plan with company matching contributions subject to eligibility requirements.
• Minimum of 15 vacation/PTO days (hourly employees receive a minimum of 120 hours) and 13 paid holidays (vacation days are prorated based on the employee's hire date within the calendar year).
• Paid sick leave adjusted based on role and location in accordance with local laws.