Staff Machine Learning Engineer, Underwriting and Credit

Amit érdemes tudni

• Irodai · Korlátozott
• Egyesült Államokbeli irodai pozíció.
• Szint: Lead
• Bitcoin-fókuszú szerep
• Fizetés: nem közölt

Helyszín és jogosultság

Egyesült Államokbeli irodai pozíció.

Bay Area, CA, United States of America

Megbízhatóság: 90%. A végső döntés a munkáltatóé – mindig olvasd el az eredeti hirdetést.

Pozíció

<p style="line-height: 1.1;" data-start="226" data-end="465">Block builds technology to increase access to the global economy. Across our ecosystem, including Square and Cash App, we create tools that help businesses run and grow, and individuals move, manage, and grow their money with confidence.</p> <p style="line-height: 1.1;" data-start="226" data-end="465">Square empowers sellers of all sizes with integrated, omnichannel tools to accept payments, manage operations, access financial services, and reach customers across online and in-person channels.</p> <p style="line-height: 1.1;" data-start="467" data-end="859">Cash App complements this by providing a fast, accessible financial platform for millions of people to send, spend, save, invest, and borrow, helping redefine how individuals interact with money.Operating at massive scale across both ecosystems means trust and safety are foundational. Our teams build systems that protect real people and businesses, safeguard financial activity, and ensure our products remain reliable, secure, and easy to use.</p> <p style="line-height: 1.1;" data-start="467" data-end="859">Block is a global, distributed company with a culture rooted in ownership, creativity, and impact. Whether supporting sellers on Square or customers on Cash App, we’re united by a shared mission: to make the global economy more accessible and inclusive.</p> <h4>The Role</h4> <p>Block has provided over $200 billion in credit to customers globally. Afterpay and Cash App Borrow are our two largest products in this space, expanding access to credit for consumers who are often underserved by traditional financial systems. Machine learning is the core of how these products work.</p> <p>Our models decide who gets credit, how much, and under what terms. They underwrite customers across a wide range of credit profiles, including many with thin or no traditional credit history. The modeling challenges are real: maintaining calibration across diverse borrower populations, designing features that generalize as the portfolio grows, and balancing approval rates against loss performance at every decision point. This requires strong fundamentals, disciplined experimentation, and continuous evaluation in production.</p> <p>On the Credit Modeling team, you will be a senior individual contributor building and evolving the ML systems behind these products. You will work across the full modeling lifecycle: problem formulation, feature development, training, cali

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