Most Loved Workplace® Certified JobStaff Machine Learning Engineer - Retention
Most Loved Workplace® Certified JobAbout the Role
At Taskrabbit, a Most Loved Workplace® certified employer in the Internet/E-Commerce space, Transforming lives, one task at a time, by connecting communities..
<div class="content-intro"><h2><span style="font-size: 18pt;"><strong>About Taskrabbit:</strong></span></h2>
<p>Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more.</p>
<p>At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love.&nbsp;Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world.</p>
<p>Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024, 2025) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed!</p></div><p><strong>This role is hybrid requiring 2 days in office at our San Francisco or NYC hub every Tuesday &amp; Wednesday.&nbsp;</strong></p>
<h3><strong>About the Role</strong></h3>
<p>Machine Learning is a cornerstone at Taskrabbit, and we're looking for a Staff Machine Learning Engineer to join our team and lead the next phase of our customer retention strategy. This is a critical, full-stack role for an individual who is passionate about the end-to-end lifecycle: from initial research and model development to building the robust systems that power <strong>repeat customer engagement and lifetime value growth at scale.</strong></p>
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