Staff Machine Learning Engineer - Retention
TaskRabbit · New York, New York, United States; San Francisco, California, United States
About this role
**About Taskrabbit** Taskrabbit is a marketplace platform that connects people with Taskers to handle everyday home to-dos—like furniture assembly, handyman work, moving help, and more. At Taskrabbit, we’re building a culture that’s collaborative, pragmatic, and fast-paced. We value innovation, inclusion, and hard work, and we’re creating more opportunities for people to earn consistent, meaningful income by building lasting relationships with clients worldwide. **Hybrid role** This role is hybrid and requires **2 days in office every Tuesday & Wednesday** at our **San Francisco or NYC hub**. --- **About the Role (Staff Machine Learning Engineer – Retention)** Machine Learning is a cornerstone at Taskrabbit. In this critical, full-stack role, you’ll lead the next phase of our **customer retention strategy**—driving **repeat customer engagement** and **lifetime value growth** at scale. Taskrabbit’s growth opportunity is deepening customer relationships and accelerating repeat purchases. Repeat customers spend **3–5x more** than one-time users, and category expansion unlocks new revenue streams within the existing customer base. --- **What You’ll Work On** - **Taskrabbit Ranking Model:** Own reliability and performance of our core ranking system; optimize **First-Time Right (FTR)** rates and tasker-to-job matching. - **Increase repeat purchase frequency** via intelligent matching, personalized recommendations, and category discovery. - **Expand customer lifetime value** by helping customers return for new service categories. - **Optimize affordability and relevance** using dynamic pricing, smart segmentation, and category-specific experiences. - **Reduce 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 full lifecycle—from feature engineering and training to evaluation, deployment, monitoring, and optimization in production. - **Infrastructure & Scalability:** Build and maintain scalable ML infrastructure and data pipelines for reproducible feature engineering and deployment across real-time, near real-time, and batch. - **Monitoring & Performance Optimization:** Develop observability for data quality and model performance; collaborate with engineering and science teams to optimize training, inference, and evaluation. - **Software Engineering Excellence:** Write clean, maintainable code; participate in code reviews, documentation, and best practices. --- **Your Areas of Expertise** - **BS/MS/PhD** in Computer Science, Statistics, Operations Research, or a related quantitative field. - **8+ years** building and deploying production-grade ML models and systems. - Strong ML knowledge, especially in **search, ranking, recommender systems, pricing/elasticity modeling, or predictive analytics**. - Strong software engineering skills (e.g., **Python**). Experience with ML libraries such as **scikit-learn, LightGBM, XGBoost, TensorFlow, PyTorch**, etc. - **SQL proficiency** for complex queries and data transformation. - Experience building **REST API-based services**. - Experience with modern data/ML technologies such as **Docker, Kubernetes, Kafka, Airflow, data warehouses** (Snowflake/Redshift/BigQuery), and **data lakes**. - **dbt** is a plus. - Familiarity with **Infrastructure as Code** (e.g., GitHub Actions) and **CI/CD** pipelines. - Excellent communica
Listing freshness
CronJobs last confirmed this listing 1h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.