Staff Machine Learning Engineer - Retention

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About the role

About Taskrabbit: 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. 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. 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. 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! This role is hybrid requiring 2 days in office at our San Francisco or NYC hub every Tuesday & Wednesday. About the Role 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 repeat customer engagement and lifetime value growth at scale. Taskrabbit's greatest growth opportunity lies in deepening customer relationships and accelerating repeat purchases. Our most valuable customers are those who return frequently, discover new service categories, and increase their spending over time. There's significant untapped potential in the marketplace: repeat customers spend 3-5x more than one-time users, and category expansion unlocks new revenue streams within our existing customer base. This role is central to capturing that opportunity. While initial matching quality and service discovery matter, the real competitive advantage and growth lever is optimizing the experience after a successful first job. What You'll Work On: • Taskrabbit Ranking Model: Own the reliability and performance of our core ranking system, ensuring accurate tasker-to-job matching and optimizing First-Time Right (FTR) rates. • Increased repeat purchase frequency through intelligent matching, personalized recommendations, and category discovery • Expanded customer lifetime value by helping customers find and return for new service categories • Optimized affordability and relevance via dynamic pricing, smart segmentation, and category-specific experiences • Reduced friction and churn through predictive quality interventions and proactive customer success • Marketplace resilience by building systems that keep high-value customers engaged and loyal • End-to-End ML Lifecycle: Own the complete lifecycle of models—from feature engineering and training through evaluation, deployment, monitoring, and optimization in production. • Infrastructure & Scalability: Build and maintain scalable, reliable ML infrastructure and data pipelines that support reproducible feature engineering and model deployment across real-time, near real-time, and batch contexts. • Monitoring & Performance Optimization: Develop monitoring and observability systems to understand data quality and model performance in complex systems. Collaborate with engineering and science teams to optimize algorithms for training, inference, and evaluation. • Software Engineering Excellence: Write clean, efficient, and maintainable code. Participate actively in c

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