$ cat jobs/staff-machine-learning-engineer-doma-technology-llc-842f6383023c.json
Staff Machine Learning Engineer
If you’re good at what you do, you can work anywhere. If you’re the best at what you do, come work for Doma Technology. About Us Doma Technology LLC offers solutions for lenders, real estate professionals, title agents, and homeowners that make closings vastly simpler and more efficient, reducing cost and increasing customer satisfaction. Our Values Obsessively Entrepreneurial - We encourage calculated risk-taking, and we know that some of our best learning happens by making mistakes along the way. People First - We communicate with honesty and respect to our customers, colleagues, and partners. Better Together - We believe diversity, equity and inclusion creates value through the differences in our backgrounds, experiences, and perspectives. Act with Integrity - We hold ourselves to the highest ethical standards in all of our business practices. 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 experi
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