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Browse Open Jobs[Remote] Machine Learning Engineer II, Burger King, US&C
Summary by AIEngineer.careers
[Remote] Machine Learning Engineer II, Burger King, US&C is a Machine Learning Engineer role (full-time). with Analytics Engineering. in REMOTE, UNITED STATES (REMOTE), US. Imported listing (source: worksynergy.10001mb.com). Apply on the employer's site (worksynergy.10001mb.com).
Imported listing (source: worksynergy.10001mb.com) · Apply on worksynergy.10001mb.com
REMOTE · UNITED STATES (REMOTE) · US
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Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from worksynergy.10001mb.com
Restaurant Brands International is seeking a Machine Learning Engineer II to develop and deploy machine learning models that optimize restaurant performance. The role focuses on causal inference, recommendation systems, and optimization models, collaborating with analytics engineering and MLOps teams to bring models into production.
Responsibilities
- Design and develop machine learning models including causal inference, recommendation systems, clustering, and optimization models
- Partner with analytics engineering to design and evaluate experiments (A/B testing, matched cohorts, difference-in-differences)
- Monitor and refine model performance using statistical methods and iterative experimentation
- Transform curated datasets into high-quality model inputs through feature engineering and validation
- Collaborate with analytics engineering, data engineering, and MLOps teams to ensure production-ready models
Requirements
- 3+ years of experience in machine learning, applied statistics, or related field
- Bachelor's or Master's degree in Statistics, Economics, Operations Research, Mathematics, Computer Science, or equivalent experience
- Strong foundation in statistical modeling and machine learning
AE
Analytics Engineering
United States (Remote) · US
Analytics Engineering is a professional discipline and role that bridges the gap between data engineering and data analysis. It focuses on transforming raw, messy data into clean, tested, documented, and reliable datasets within a data warehouse, utilizing software engineering best practices such as version control and continuous integration to empower end users to perform their own analysis.
