Lead Machine Learning Engineer - REMOTE

Role Overview

Lead Machine Learning Engineer (REMOTE)

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

Responsibilities

• Design and build the ML platform surface for data science teams, including:
  • Model packaging
  • Deployment
  • Batch and real-time inference
  • Observability
• Establish ML platform standards and reusable components, raising the engineering bar across the organization.
• Serve as technical authority on MLOps decisions; mentor data scientists and engineers and review platform-adjacent work.
• Own model serving infrastructure on AWS SageMaker (including SageMaker Unified Studio):
  • Batch inference patterns
  • Real-time endpoints
  • Serverless inference based on workload
• Build and maintain model registry and versioning workflows (dev → staging → production) with lineage and auditability.
• Stand up retraining pipelines using:
  • MLflow
  • Weights & Biases
  • Orchestration tooling
  • Include retraining triggers, experiment tracking, evaluation, and approval gates
• Operate production monitoring and alerting for:
  • Drift
  • Performance degradation
  • Data quality issues
  • Latency/cost anomalies
• Write clean, modular Python and Infrastructure as Code using Terraform, applying engineering best practices (testing, versioning, code review).
• Partner with data scientists and platform teams to improve workflow reliability and reduce time-to-production.
• Ensure feature pipelines, model artifacts, and inference services integrate cleanly with broader data/AI platforms.

Requirements

• Bachelor’s degree or higher in Computer Science, Engineering, or related technical field.
• 7+ years of software engineering experience with meaningful production ownership of services/platforms in a cloud environment.
• 5+ years (cut off in the provided text) with additional relevant experience (likely ML/MLOps and/or platform engineering).