Staff Machine Learning Engineer, Money (Founding ML Lead)

<div class="content-intro"><p><img style="display: none; max-width: 100%;" src="https://click.appcast.io/greenhouse-te8/a31.png?ent=34e=22630t=1701374353806" width="1px"> <img style="display: none; max-width: 100%;" src="https://track.jobadx.com/v1/i.gif?utm_pixel=224e990b-8ff4-4287-8d5d-2ff09647f181utm_ptz=ESTutm_rqt=track" alt="" width="1"></p></div><h2 data-prosemirror-content-type="node" data-prosemirror-node-name="heading" data-prosemirror-node-block="true" data-pm-slice="1 1 []"><strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">About the Team</strong></h2> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">The <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">Money org</strong> at DoorDash builds the financial products and platforms that power our marketplace — from helping merchants grow their businesses with access to capital, to enabling banking products for Dashers, to optimizing consumer payments and checkout experiences.</p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">We are forming a <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">new Money Machine Learning team</strong>, and this role will be the <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">founding ML engineer</strong>. The team’s initial focus will be on <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">underwriting and decisioning for merchant cash advances and lending products</strong>, with a broader mandate over time to support other high-impact Money initiatives, including:</p> <ul> <li>Merchant, Dasher and Consumer financial products (banking, payouts, gift cards, risk)</li> <li>Consumer payments optimization (authorization rates, routing, cost efficiency)</li> <li>Platform-level ML capabilities for credit and financial decisioning</li> </ul> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true" data-pm-slice="1 1 []">This team will sit at the intersection of <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">fintech bets and the Money platform</strong>, building reusable ML systems that scale across products. Over time, this role has the opportunity to <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">grow and shape a team</strong>, setting technical direction and best practices for financial ML at DoorDash.</p> <h2 data-prosemirror-content-type="node" data-prosemirror-node-name="heading" data-prosemirror-node-block="true" data-pm-slice="1 1 []"><strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">About the Role</strong></h2> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">We’re looking for a <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">Staff Machine Learning Engineer</strong> to lead the design, development, and deployment of <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">production-grade ML systems</strong> that drive financial decisioning across DoorDash’s Money ecosystem.</p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">You will start by owning ML-driven <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">underwriting models for merchant cash advances</strong>, partnering closely with Product, Risk, Data Science, and Engineering to improve approval rates, loss performance, and capital efficiency. As the Money ML surface area expands, you will broaden your scope to support additional initiatives such as <strong data-prosemirror-content-type="mark" data-prosemirror-mark-name="strong">ban ... (truncated, view full listing at source)