Job Title: Staff ML Engineer 

About the Role 

We’re hiring a Staff ML Engineer to build and deploy production-grade machine learning systems that reshape decision-making in the title insurance and real estate sectors. You’ll own the development of robust, scalable ML products that power risk assessment, streamline underwriting, and improve operational efficiency. This role blends deep technical execution with cross-functional collaboration and offers a unique opportunity to apply advanced ML techniques in a highly regulated, high-impact domain. 

What You’ll Do 

• Design, build, and maintain scalable ML systems across the full model lifecycle—from data ingestion and training to deployment, monitoring, and retraining. 
• Serve as a go-to expert in title risk and underwriting for our team and the company. This includes exploring ways to reduce model losses, improve the fidelity of our projected losses, and identify new risks in our domain. 
• Collaborate with data engineers, product teams, and domain experts to translate business goals into ML solutions that perform in real-world production environments. 
• Conduct ad-hoc analyses and experimentation to inform modeling decisions and stakeholder strategy. 

Who You Are 

• An Owner: You drive work forward with minimal oversight and proactively address challenges. 
• Curious: You continuously learn and seek to improve tools, systems, and outcomes—especially in AI and engineering domains. 
• Clear Communicator: You can explain technical decisions and concepts to diverse stakeholders. 
• Product Oriented: You care about measurable impact and shipping reliable, real-world solutions. 

Qualifications 

• 6+ years developing and deploying models in a cloud-based production environment following MLOps and engineering best practices, with proficiency in Python, SQL, Git, and Docker 
• Depth in supervised learning on tabular data — feature engineering, class imbalance, label definition, and threshold selection with the business tradeoffs behind it. 
• Experience monitoring and evaluating models in production over time — tracking degradation, drift, and error rates, and turning that into a defensible account of what changed and why 
• Strong applied statistics. You can decompose a metric change into its drivers and defend the decomposition. 
• You're the reviewer, not the reviewed. You look for the reason a number might be wrong before a client's reviewer does, and you have a track record of catching problems in other people's analyses and raising the bar around you. 
• Fluent with current AI tooling in your own work. You already heavily use LLM-based coding and analysis tools, you have opinions about where they help and where they fail, and you keep up with what's changing. We're not looking to persuade anyone that these tools are worth using. 
• Track record of owning complex, ambiguous projects end to end and working directly with product and senior leadership — including pushing back on requirements when they're wrong