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Salary: USD 170,000 - 225,000 / annual
Taskrabbit is a marketplace platform connecting people with Taskers for home services like furniture assembly, handyman work, and moving help. The company is owned by IKEA and operates across the US and EU with a hybrid, distributed workforce.
You will join as a Staff Machine Learning Engineer focused on customer retention and lifetime value growth. This is a full-stack role owning the complete ML lifecycle—from research and model development through production deployment, monitoring, and optimization. The role is central to Taskrabbit's growth strategy: repeat customers spend 3–5x more than one-time users, and the company sees significant untapped potential in deepening customer relationships and expanding service categories within the existing base.
Key responsibilities include:
• Owning the reliability and performance of the core ranking system, ensuring accurate tasker-to-job matching and optimizing First-Time Right (FTR) rates.
• Driving increased repeat purchase frequency through intelligent matching, personalized recommendations, and category discovery.
• Expanding customer lifetime value by helping customers find and return for new service categories.
• Optimizing affordability and relevance via dynamic pricing, smart segmentation, and category-specific experiences.
• Reducing friction and churn through predictive quality interventions and proactive customer success.
• Building marketplace resilience systems that keep high-value customers engaged and loyal.
• Owning the end-to-end ML lifecycle: feature engineering, training, evaluation, deployment, monitoring, and optimization in production.
• Building and maintaining scalable, reliable ML infrastructure and data pipelines supporting reproducible feature engineering and model deployment across real-time, near real-time, and batch contexts.
• Developing monitoring and observability systems to understand data quality and model performance in complex systems.
• Writing clean, efficient, maintainable code and participating actively in code reviews and best practices.
This role is hybrid, requiring 2 days in office (Tuesdays & Wednesdays) at either the San Francisco or NYC hub.
REQUIREMENTS:
• 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. Experience with popular ML libraries such as Scikit-learn, LightGBM, XGBoost, TensorFlow, or PyTorch.
• Proficiency in SQL for writing complex queries and transforming data.
• Experience building REST API-based services.
• Experience with modern data and ML technologies: Docker, Kubernetes, Kafka, Airflow, data warehouses (Snowflake, Redshift, BigQuery), and data lakes.
• Familiarity with dbt for transforming and testing data (plus).
• Familiarity with Infrastructure as Code tools 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.
• Passion for quickly learning new technologies and drive to solve challenging problems; collaborative mindset.
• Ideally, experience working in marketplace or platform contexts where ranking, matching, and pricing directly impact user experience and business outcomes.
