Manager, Machine Learning Engineer

Who is Taco Bell?

Taco Bell was born and raised in California and has been around since 1962. We went from selling everyone’s favorite Crunchy Tacos on the West Coast to a global brand with 8,200+ restaurants, 350 franchise organizations, that serve 42+ million fans each week around the globe. We’re not only the largest Mexican-inspired quick service brand (QSR) in the world, we’re also part of the biggest restaurant group in the world: Yum! Brands. 

Much of our fan love and authentic connection with our communities are rooted in being rebels with a cause. From ensuring we use high quality, sustainable ingredients to elevating restaurant technology in ways that hasn’t been done before… we will continue to be inclusive, bold, challenge the status quo and push industry boundaries.   

We’re a company that celebrates and advocates for different, has bold self-expression, strives for a better future, and brings the fun while we’re at it. We fuel our culture with real people who bring unique experiences. We inspire and enable our teams and the world to Live Más.

At Taco Bell, we’re Cultural Rebels. Want to join in on the passion-fueled fun? Learn more about the career below. 

About the Job:

As the Machine Learning Engineer Manager, you will lead a team responsible for deploying, monitoring, and managing machine learning models in production environments to solve business problems. You will collaborate closely with data scientists, data engineers, and data operations teams to ensure the reliability, scalability, and performance of machine learning systems. Your role involves overseeing the end-to-end machine learning lifecycle, implementing best practices for model deployment, automation, and monitoring, and driving continuous improvement in MLOps processes.

The Day-to-Day:

• Design, lead, and manage a team of MLOps engineers, providing guidance, mentorship, and performance management to ensure the successful delivery of MLOps initiatives utilizing cutting edge tools and techniques.
• Define and implement MLOps strategies, processes, and standards to streamline the deployment, monitoring, and management of machine learning models in production environments.
• Design and implement scalable, automated pipelines for model training, testing, deployment, and inference, leveraging infrastructure as code (IaC) and continuous integration/continuous deployment (CI/CD) practices.
• Establish monitoring and alerting systems to track model performance, data drift, and system health, ensuring timely detection and resolution of issues in production ML workflows.
• Implement robust version control and model governance processes to manage model artifacts, dependencies, and configurations throughout the ML lifecycle.
• Drive optimization initiatives to improve the efficiency, reliability, and cost-effectiveness of MLOps infrastructure and workflows, leveraging cloud services and automation tools and able to write testable, reusable high-quality code and build the capability in team members.
• Establish key performance indicators (KPIs) and metrics to measure the effectiveness and impact of MLOps initiatives, and provide regular reports and updates to senior leadership.
• Foster a culture of collaboration, innovation, and excellence within the MLOps team, promoting knowledge sharing, skill development, and continuous learning.

Is This You?

• Bachelor's degree in Computer Science, Engineering, Data Science, or related field; advanced degree preferred.
• 8+ years of experience in software engineering, DevOps, or data engineering roles, with at least 3 years in a MLOps leadership or managerial capacity.
• Strong background in machine learning, data science, and AI technologies, with hands-on experience deploying and managing machine learning models in production environments.
• Proficiency in programming languages such as Python, Java, or Scala, and experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Expertise in cloud platforms in AWS is required or other Cloud Platform (Google Cloud or Microsoft Azure).
• Proficiency in containerization technologies (e.g., Docker, Kubernetes), with experience in designing and implementing scalable, cloud-native ML solutions.
• Solid understanding of DevOps principles, CI/CD pipelines, version control systems (e.g., Git), and infrastructure automation tools (e.g., Terraform, Ansible).
• Strong analytical, problem-solving, and communication skills, with the ability to translate business requirements into technical solutions and influence cross-functional teams.
• Experience with agile methodologies, project management practices, and agi