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# Director of Machine Learning Engineering

- **Company:** CoreLogic, Inc.
- **Location:** Dallas, United States
- **Experience:** Experienced
- **Salary:** $162,400.0 - $195,000.0
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Artificial Intelligence, Algorithm Design, Automation of Tests, BigQuery, Continuous Integration, Data Governance, Distributed Computing Environment, Distributed Systems, Data Flow Control, Machine Learning, Software Deployment, Software Engineering, Google Cloud, Feature Engineering, Data Strategy, Machine Learning Operations, Software Version Control, Data Pipelines
- **Published:** June 9, 2026
- **Apply:** https://www.dice.com/job-detail/585a84a3-d146-4262-9dee-4f4182e2f801

## About the Role

The ideal candidate is a seasoned people leader who thrives at the intersection of large-scale distributed systems and advanced statistical modeling, with a proven track record of shipping ML products within the Google Cloud Platform (Google Cloud Platform) ecosystem., * Strategic Leadership: 8+ years of experience in ML or Software Engineering, with at least 3+ years in a dedicated people management role. You have a proven track record of scaling high-output teams and mentoring Staff-level engineers in the analytics or machine learning space.

 * Advanced ML Architecture: Deep hands-on expertise in the full ML lifecycle-from research and algorithm development to feature engineering and distributed data processing. You don't just build models; you design the systems that make them reproducible and scalable.
 * Google Cloud Platform Ecosystem Mastery: Architect-level command of Google Cloud Platform. You should be proficient in leveraging Vertex AI, BigQuery ML, and Dataflow to build cost-effective, high-availability ML infrastructure.
 * Domain Expertise: Specialized experience with Automated Valuation Models (AVM) or high-stakes predictive modeling within Real Estate/FinTech. You understand the nuances of geospatial data, market volatility, and valuation accuracy.
 * Business-Technical Synthesis: Exceptional ability to bridge the gap between executive strategy and technical execution. You can translate ambiguous business goals into rigorous technical roadmaps and ROI-driven engineering projects.
 * Operational Excellence: Strong advocate for MLOps best practices, including CI/CD for machine learning, automated model monitoring, and robust data governance.
 * Communication & Influence: Masterful interpersonal skills with the ability to influence stakeholders at the C-suite level and foster a collaborative environment across cross-functional product and data science squads.

## Description

We are seeking a visionary Director of Machine Learning Engineering to lead a high-performing team of ML engineers and MLOps specialists. This leader will bridge the gap between data science and production, ensuring our proprietary Automated Valuation Models (AVMs) are scalable, performant, and reliable., * Strategic Leadership: Mentor and scale a dual-discipline team of ML Engineers and Operations specialists, fostering a culture of technical excellence and rigorous engineering standards.

 * AVM Architecture: Direct the end-to-end lifecycle of custom Automated Valuation Models, from architectural design in Google Cloud Platform to production deployment and real-time inference.
 * MLOps Excellence: Drive the adoption of CI/CD for ML (CT - Continuous Training), ensuring robust model versioning, automated testing, and seamless deployment via Vertex AI or GKE.
 * Data Strategy & Lineage: Oversee the engineering of automated feature stores and data pipelines, ensuring high-fidelity datasets for training, validation, and backtesting.
 * Performance & Scalability: Partner with Data Science and Product teams to solve bottlenecks in model latency, throughput, and cost-efficiency.
 * Quality Assurance: Implement sophisticated monitoring frameworks to detect feature drift and model decay, ensuring the long-term accuracy of valuation outputs.
 * Stakeholder Management: Translate complex technical roadmaps into actionable business value for executive leadership and cross-functional partners.


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