Lead Machine Learning Engineer

Domino's

Ann Arbor Charter Township (MI)

On-site

USD 130,000 - 180,000

Full time

7 days ago

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Benefits offered by this job

Paid Holidays and Vacation

Medical, Dental & Vision benefits that

No-cost mental health support for员工及家属

Childcare tuition discounts

No-cost fitness, nutrition, and well-

Fertility benefits

Adoption assistance

401k matching contributions

15% off the purchase price of stock

Company bonus

Job summary

Domino’s is seeking a senior ML engineer to lead design, development, and deployment of machine learning models addressing complex business challenges. You will guide engineers and data scientists, translate business needs into scalable AI solutions, and drive best practices across MLOps and cloud-native platforms.

The role emphasizes collaboration across teams, design of scalable ML architectures, and ongoing evaluation of new AI technologies to accelerate business value.

Qualifications

5 to 8 years of professional experience in machine learning engineering or related fields.
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical discipline; advanced degrees preferred.

Responsibilities

Serve as a senior technical resource for Machine Learning, AI, and Generative AI initiatives.
Provide technical leadership and mentorship to engineers and data scientists.
Translate business requirements into scalable, maintainable technical solutions.
Promote engineering best practices across software development, MLOps, and cloud-native platforms.
Collaborate with cross-functional teams to deliver business value through AI and data-driven solutions.
Contribute to the design and evolution of scalable ML/AI architectures and operational processes.
Evaluate emerging technologies and recommend opportunities for adoption when appropriate.

Skills

Machine Learning

Python

TensorFlow

PyTorch

scikit-learn

Leadership

Education

Bachelor's degree in CS/DS/Engineering

Tools

Azure ML

Databricks

GitHub

Docker

Kubernetes

Job description

Location Name - Location Code: Home Office - Washington-DPIWA

Company Description

Domino’s Pizza, which began in 1960 as a single store location in Ypsilanti, MI, has had a lot to celebrate lately: we’re a reshaped, reenergized brand of honesty, transparency and accountability - not to mention, great food! In the rise to becoming a true technology leader, the brand is now consistently one of the top five companies in online transactions and 85% of our sales in the U.S. are taken through digital channels. The brand continues to 'deliver the dream' to local business owners, 90% of which started as delivery drivers and pizza makers in our stores. That’s just the tip of the iceberg…or as we might say, one 'slice' of the pie! If this sounds like a brand you’d like to be a part of, consider joining our team!

Job Description

Leads the design, development, and deployment of machine learning models to address moderately complex to complex business challenges. Applies advanced technical expertise and organizational knowledge to independently solve problems that may lack well-defined solutions, ensuring alignment with strategic objectives and operational requirements.

Responsibilities

Serve as a senior technical resource for Machine Learning, AI, and Generative AI initiatives.
Provide technical leadership and mentorship to engineers and data scientists.
Translate business requirements into scalable, maintainable technical solutions.
Promote engineering best practices across software development, MLOps, and cloud-native platforms.
Collaborate with cross-functional teams to deliver business value through AI and data-driven solutions.
Contribute to the design and evolution of scalable ML/AI architectures and operational processes.
Evaluate emerging technologies and recommend opportunities for adoption when appropriate.

Job Tasks

Conduct code reviews, design discussions, and technical troubleshooting sessions.
Design, develop, deploy, and support production ML and AI solutions using Azure, Databricks, and containerized services.
Build reusable frameworks, APIs, deployment patterns, and automation capabilities.
Partner with architects, engineers, and platform teams to deliver secure, scalable, and maintainable solutions.
Develop and operationalize machine learning and Generative AI solutions that support business objectives.
Integrate AI services, APIs, and enterprise data sources into production applications and workflows.
Contribute to model deployment, monitoring, and lifecycle management activities.
Participate in Agile ceremonies, technical planning, and solution design reviews.
Contribute to shared code repositories through peer reviews, testing, and continuous integration practices.
Support production deployments, incident response, troubleshooting, and root cause analysis efforts.

Qualifications

5 to 8 years of professional experience in machine learning engineering or related fields demonstrating mastery of advanced technical skills and independent project leadership.
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical discipline; advanced degrees preferred.

Skills

Advanced proficiency in machine learning algorithms, model development, and evaluation techniques.
Strong programming skills in Python and experience with machine learning libraries such as TensorFlow, PyTorch, or scikit-learn.
Ability to independently solve complex problems with limited guidance, leveraging deep organizational and functional knowledge.
Effective communication skills, with the ability to articulate technical concepts and collaborate across teams.
Experience designing scalable machine learning systems and integrating them into production workflows.

Software Application

Azure Machine Learning for model development, deployment, monitoring, and lifecycle management
Databricks for data engineering, machine learning, and large-scale analytics
GitHub (GitHub Enterprise, Actions, and repository management) for source control, CI/CD, and DevOps automation
Python and associated ML libraries (TensorFlow, PyTorch, scikit-learn, Pandas, NumPy)
Containerization and orchestration technologies (Docker, Kubernetes, Azure Container Apps)
REST APIs and integration frameworks for connecting AI services, enterprise applications, and data platforms

Benefits