Staff Machine Learning Engineer

Who you are

• 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

What the job involves

• 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
• 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

Benefits

• Remote first and highly connected
• Employee-covered health care
• 401k with match
• Flexible PTO
• Paid holidays & recharge weeks
• Wellbeing allowance and free access to HeadSpace and Spring Health
• Paid parental leave, adoption assistance, and reproductive health assistance
• Speaker series, career paths, and an annual education allowance
• IKEA discount, Taskrabbit credit, and productivity stipend