ML Platform Engineer is an exciting remote opportunity at Sundayy, based Remote. This role is ideal for AI professionals looking to work from home while contributing to cutting-edge artificial intelligence projects. Read the full job description below and apply today.
About The Company
Lennar is one of the nation's leading homebuilders, renowned for its commitment to quality, innovation, and customer satisfaction. With a rich history of building homes that turn dreams into reality, Lennar focuses on creating exceptional living experiences for homeowners, communities, and associates alike. The company emphasizes giving back to the communities in which it operates and fostering a culture of opportunity, growth, and career development for its employees. Recognized as a Fortune 500® company and consistently ranked among the top homebuilders in the United States, Lennar continues to set industry standards through its dedication to excellence and sustainable growth.
About The Role
We are seeking a highly skilled Lead Machine Learning (ML) Engineer to join our remote team. This pivotal role involves owning and evolving the infrastructure that supports our data science and ML models, ensuring seamless transition from development to production. Sitting at the intersection of software engineering, ML platform management, and applied data science, the ideal candidate will possess deep expertise in MLOps and a strong background in cloud-based ML deployment, particularly within AWS SageMaker. The Lead ML Engineer will collaborate closely with data scientists, AI engineers, and platform teams to build and maintain scalable, reliable, and efficient ML systems. This role offers the opportunity to influence the entire organization by establishing standards, mentoring teams, and driving innovation in ML operations, ultimately enabling faster deployment, retraining, and operational excellence across multiple divisions.
Qualifications
Bachelor’s degree or higher in Computer Science, Engineering, or a related technical field
7+ years of professional software engineering experience, including ownership of services or platforms in a cloud environment
5+ years of hands-on experience with MLOps or ML platform development, deploying and monitoring models at scale
Extensive experience with AWS SageMaker, including SageMaker Unified Studio, training jobs, endpoints, and pipelines
Proficiency with experiment tracking tools such as MLflow, Weights & Biases, or similar
Strong Python programming skills, with a focus on modular, well-tested, production-ready code
Experience with infrastructure-as-code tools such as Terraform
Deep understanding of batch and real-time inference patterns, including tradeoffs and operational considerations
Proven ability to collaborate effectively with data scientists and cross-functional teams
Ability to work independently in ambiguous environments, prioritize tasks, and proactively address blockers
Bonus qualifications include experience with feature stores, GPU workloads, distributed training, model drift monitoring, and supporting classical ML and LLM-based models
Responsibilities
Design, develop, and maintain the ML platform surface used by data science teams, including model packaging, deployment, inference, and observability
Establish and promote standards, reusable components, and best practices for ML model development, deployment, and operation
Mentor data scientists and engineers, conducting code reviews and providing technical guidance on MLOps practices
Manage model serving infrastructure on AWS SageMaker, including batch inference, real-time endpoints, and serverless options based on workload needs
Build and oversee the model registry, version control, and promotion workflows ensuring full lineage and auditability
Develop and operate retraining pipelines using tools like MLflow and Weights & Biases, automating triggers, experiments, and approval processes
Implement monitoring and alerting systems for production models, focusing on drift detection, performance metrics, data quality, latency, and cost management
Write clean, modular code using Python and infrastructure-as-code (Terraform), adhering to best practices including testing and version control
Collaborate with data scientists to streamline workflows, reduce time-to-production, and enhance model reliability
Partner with data and platform engineering teams to ensure seamless integration of feature pipelines, model artifacts, and inference services within the broader data ecosystem
Benefits
The opportunity to make a significant impact across one of the largest homebuilders in the United States
A corporate culture dedicated to growth, development, and innovation
Flexibility to try new ideas and deploy impactful solutions
End-to-end project ownership with visibility across multiple divisions
Remote work arrangement, with preferred locations including Miami, FL; Bentonville, AR; or Dallas, TX
Comprehensive health benefits including medical, dental, and vision insurance
401(k) matching program to support financial security
Paid parental leave and associate assistance programs
Education assistance and adoption support up to $30,000
Generous paid time off including vacation, holidays, sick leave, and personal d
