Sr Manager, Machine Learning Engineering

McDonald's Corporation•Chicago, IL

About The Position

McDonald’s is scaling its global data platform to deliver real-time, actionable insights that enhance operations and elevate the customer experience. Through Enterprise Data, Analytics, and AI (EDAA), we’re enabling a smarter, more connected ecosystem—driven by cloud technology, automation, and intelligent data engineering. We are looking for a Senior Machine Learning Engineer with strong technical expertise to design, build, and deploy scalable ML/AI solutions. In this role, you will be responsible for developing robust machine learning pipelines, optimizing model performance, and contributing to the design of ML/AI products that drive real business impact. You will collaborate closely with data scientists, product managers, and platform engineers to bring advanced ML capabilities into production.

Requirements

Master’s and plus degree in Computer Science, Engineering, Mathematics, or a related technical field (PhD is a plus).
5+ years of hands-on experience in machine learning, AI engineering, or related fields.
Strong proficiency in Python and common ML libraries such as scikit-learn, TensorFlow, PyTorch.
Solid understanding of ML concepts including feature engineering, model selection, hyperparameter tuning, and evaluation metrics.
Experience building production-grade ML systems, including model serving and monitoring.
Proficiency with modern MLOps tools (e.g., MLflow, Kubeflow, Airflow, CI/CD frameworks).
Experience with cloud platforms such as Google Cloud Platform, Amazon Web Services.
Strong problem-solving skills and the ability to work independently on complex technical challenges.

Nice To Haves

Familiar with McDonald’s business
Experience with time series forecasting, LLMs, or AI agent frameworks.
Familiarity with distributed training and large-scale model deployment.
Experience applying advanced techniques such as reinforcement learning, generative AI, or retrieval-augmented generation (RAG).
Contributions to open-source ML frameworks or research publications.

Responsibilities

Partner with product and business teams to define problems and translate them into data-driven solutions.
Conduct exploratory data analysis (EDA) and extract actionable insights from structured and unstructured datasets.
Develop, validate, and iterate on predictive models using techniques in supervised, unsupervised, and/or time series learning.
Communicate modeling outcomes through clear visualizations and presentations to both technical and non-technical stakeholders.
Design, build, and optimize end-to-end machine learning pipelines — from data ingestion and feature engineering to model training, evaluation, and deployment.
Develop and maintain scalable ML infrastructure to support both batch and real-time inference.
Build high-performing models for forecasting, prediction, recommendation, or intelligent automation, depending on business use cases.
Collaborate with cross-functional teams to translate business problems into effective ML solutions.
Conduct feature selection, model tuning, and performance optimization to ensure production-grade reliability and scalability.
Implement monitoring, retraining, and evaluation strategies to maintain model quality over time.
Explore and apply state-of-the-art ML/AI methods, including deep learning, generative AI, and agentic frameworks where applicable.
Ensure best practices in ML engineering, including version control, CI/CD, MLOps, and documentation.