Lead Machine Learning Engineer - REMOTE

Lead ML Engineer - REMOTE

We are Lennar

Lennar is one of the nation's leading homebuilders, dedicated to making an impact and creating an extraordinary experience for their Homeowners, Communities, and Associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work and live in, and fostering a culture of opportunity and growth for our Associates throughout their career. Lennar has been recognized as a Fortune 500® company and consistently ranked among the top homebuilders in the United States.

Join a Company that Empowers you to Build your Future

Lennar is seeking a Machine Learning Engineer to own and evolve the infrastructure and surface mechanisms that take our data science and ML models from notebook to production. This is a key role on the Applied AI & Data Science team, sitting at the intersection of software engineering, ML platform, and applied data science.

The ideal candidate is a software engineer with deep MLOps expertise. They know how to design model serving for both batch and real-time inference, build durable model registries and versioning practices, and stand up retraining pipelines that data scientists actually use. They are hands-on with AWS SageMaker (including SageMaker Unified Studio), MLflow, Weights & Biases, and the surrounding tooling that makes ML systems reliable in production.

You’ll partner closely with data scientists, AI engineers, and platform teams— building and setting the foundation that lets ML models ship faster, retrain on schedule, and operate with the same engineering rigor as any other production service across 40+ divisions of one of the nation’s largest homebuilders.

A career with purpose.

A career built on making dreams come true.

A career built on building zero defect homes, cost management, and adherence to schedules.

Your Responsibilities on the Team

Design, build, and set the ML platform surface used by our data science team—covering model packaging, deployment, batch and real-time inference, and observability.

Establish and evangelize ML platform standards, patterns, and reusable components—raising the engineering bar for how ML models are built, deployed, and operated across the organization.

Mentor data scientists and engineers on production ML practices, code review their platform-adjacent work, and serve as the technical authority on MLOps decisions.

Own model serving infrastructure on AWS SageMaker (including SageMaker Unified Studio)—building patterns for batch inference jobs, real-time endpoints, and serverless inference depending on workload requirements.

Build and maintain the model registry, version control, and promotion workflows that move models cleanly from development to staging to production with full lineage and auditability.

Stand up and operate retra