Staff Machine Learning Engineer

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! Prior to applying please note: W e are currently unable to provide visa sponsorship for this position (including H-1B, OPT, or other employment-based visas). Candidates must be legally authorized to work in the United States without employer sponsorship now or in the future. This role is hybrid requiring 2 days in office at our San Francisco hub every Tuesday & Wednesday (located at 130 Sutter St). About the Role Machine Learning is a cornerstone at Taskrabbit, and we’re looking for a Staff Machine Learning Engineer to take technical ownership of our core ranking system. Every job request on the platform flows through it, making this one of the most consequential ML systems we run. This is a hands-on technical leadership role. You’ll operate as the primary architect and engineer for the ranking system — defining the system direction, driving the roadmap, solving the hardest problems, and creating leverage for the engineers around you. You’ll also serve as the primary technical interface for Product, Data Science, Risk, and Commerce on all things matching. What we need is a single technical owner who can hold the architecture, translate strategy into engineering action, and mentor the team as the system scales. What You’ll Work On: Ranking System Ownership: Own the architecture, model design, experiment strategy, and production reliability of the core ranking system end-to-end. You are the system’s primary technical decision-maker. Cross-functional Technical Leadership: Serve as the primary technical point of contact for Product, Data Science, Risk, and Commerce on matching and ranking questions. Actively participate in architecture reviews; give clear, well-reasoned recommendations and convince stakeholders on technical tradeoffs. Model Reliability: Build automated retraining pipelines, rollback capability, and skew detection — the foundations that enable safe experimentation at speed and eliminate silent model drift. Ranking Quality & Debiasing: Address position bias and other systemic biases in the ranking pipeline to produce cleaner training data and unlock downstream experimentation. Client Intent Signals: Leverage filter-based signals from high-booking user cohorts (69–72% higher booking rate) to drive direct conversion lift from untapped behavioral data. Personalization: Surface return-client signals, prior Tasker preferences, and preference modeling to improve IAR for the highest-value booking segment. Platform Thinking: Identify where ML can add value beyond the ranking system and scope those opportunities for the broader team. Make build-vs-buy recommendations and contribute to long-term ML architecture at Taskrabbit. Team Development & Thought Leadership: Mentor the junior engineer, elevate seniors through code and design reviews, and raise the technical bar across the team. Beyond day-to-day guidance, you bring a transformational perspective — introducing new methodologies, challenging e…