This role is hybrid requiring 2 days in office at our San Francisco or NYC hub every Tuesday & Wednesday. About the RoleMachine 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 code reviews, documentation, and best practices across the full software engineering lifecycle.
Your Areas of Expertise:We welcome applicants from a variety of backgrounds and experiences. Below gives you a sense of how we're thinking about what you'll need to be successful in the role.
- BS, MS, or PhD in Computer Science, Statistics, Operations Research, or a related quantitative field.
- 8+ years of industry experience building and deploying high-quality, production-grade machine learning models and systems.
- Strong theoretical knowledge and hands-on experience in machine learning, particularly in search, ranking, recommender systems, pricing/elasticity modeling, or predictive analytics.
- Solid software engineering skills with proficiency in one or more programming languages, including Python. The candidate should have experience with popular ML libraries like Scikit-learn, lightgbm, xgboost, TensorFlow, PyTorch, etc.
- Proficiency in SQL is also required for writing complex queries and transforming data.
- Experience building REST API-based services.
- Experience with modern data and ML technologies, such as Docker, Kubernetes, Kafka, Airflow, data warehouses (eg snowflake, redshift or BigQuery), and data lakes.
- Familiarity with dbt is a plus for transforming and testing data.
- Familiarity with tools for Infrastructure as Code, such as Github actions, and CI/CD pipelines.
- Excellent communication skills, with the ability to present complex findings and recommendations clearly to both technical and non-technical audiences.
- A passion for quickly learning new technologies and a drive to solve challenging problems, and a collaborative mindset.
- Ideally, experience working in marketplace or platform contexts where ranking, matching, and pricing directly impact user experience and business outcomes.
Compensation & Benefits:At Taskrabbit, our approach to compensation is designed to be competitive, transparent, and equitable. Total compensation consists of base pay + bonus + benefits + perks. The base pay range for this position is $170,000 - $225,000. This range is representative of base pay only, and does not include any other total cash compensation amounts, such as company bonus or benefits. Final offer amounts may vary from the amounts listed above and will be determined by factors including, but not limited to, relevant experience, qualifications, geography, and level.
You'll love working here because:- Taskrabbit is a Hybrid Company. We value flexibility and choice but also stay committed to regular in-person connection.
- The People. You will be surrounded by some of the most talented, supportive, smart, and kind leaders and teams -- people you can be proud to work with!
- The Diverse Culture. We believe that we make better decisions when our workforce reflects the diversity of the communities in which we operate. Women make up half of our leadership team and our diversity representation is above that of the tech industry average.
- The Perks. Taskrabbit offers our employees with employer-paid health insurance and a 401k match with immediate vesting for our US based employees. We offer all of our global employees generous and flexible time off with 2 company-wide closure weeks, Taskrabbit product stipends, wellness + productivity + education stipends, IKEA discounts, reproductive health support, and more. Benefits vary by country of employment.
