Sr Machine Learning Engineer - Labor Management Forecasting (Hybrid)

Publix Super Markets, Inc., the largest employee-owned company in the U.S., is driven by a dynamic technology team of 2,300+ professionals delivering innovative solutions to 1,400+ stores and 260,000+ associates across 8 states.

From IT security and platform engineering to architecture, software development, and infrastructure, we offer career opportunities at every level—internships through technical leadership.

Join a company consistently ranked among Fortune’s “100 Best Companies to Work For” and help us build more than great subs—build the future of technology at Publix.

Join our Labor Management technology team to build innovative machine learning solutions that improve how we forecast labor needs and plan our retail workforce.

Location

Hybrid (Lakeland, FL): To support our hybrid work model, candidates must reside, or be willing to relocate, within a commutable distance of Lakeland, FL.

Work Model

Enjoy the best of both worlds - collaborate in person and innovate remotely.

Interview Process

Candidates selected to advance in the interview process will be required to participate in at least one onsite interview at our Lakeland, FL campus.

Why Join Us?* Hybrid Flexibility: Work remotely when you need focus time and join us onsite for high-impact collaboration and brainstorming sessions.

• Operational Efficiency: Ensure technology solutions support efficient workflows and enable automation to improve operational effectiveness.
• Cutting-Edge AI Projects: Drive innovation in AI platforms, integrating advanced tools and frameworks to solve complex business challenges.
• Empowered Culture: We value autonomy, creativity, and continuous learning - your ideas shape the future of technology in our organization.

Your Impact* Develop and maintain machine learning and forecasting solutions using Python and SQL.

• Build scalable ML and data pipelines using Databricks, Spark/PySpark, and Delta Lake.
• Implement MLOps practices for model training, deployment, versioning, monitoring, and retraining.
• Use MLflow and related technologies to manage the machine learning lifecycle.
• Develop automated deployment processes using Databricks Asset Bundles (DABs) and CI/CD.
• Partner with data scientists, cloud engineers, and business teams to produce labor and demand forecasting models.
• Monitor model and pipeline performance and troubleshoot production issues.
• Apply software engineering practices to create reliable, maintainable, and testable ML solutions.

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