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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** TaskRabbit - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $170,000.0 - $225,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, BigQuery, Code Review, Data Transformation, Data Warehousing, Database Queries, Github, Python (Programming Language), Machine Learning, Performance Tuning, Recommender Systems, Tensorflow, Software Engineering, SQL Databases, Feature Engineering, Pytorch, Snowflake, Data Lakes, Scikit Learn, Kubernetes, Information Technology, Xgboost, Apache Kafka, Machine Learning Operations, Restful APIs, Marketplace, Data Pipelines, Docker, Programming Languages - **Published:** September 15, 2026 - **Apply:** https://www.thejobnetwork.com/job/e8a0169f-4219-47ac-a1db-96b3ad4589d1/staff-machine-learning-engineer-retention ## About the Role - **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. , - 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. ## Description - **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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Shoot for the moon - machine learning for automated online ad detection](https://www.wearedevelopers.com/videos/502-shoot-for-the-moon-machine-learning-for-automated-online-ad-detection) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [The pitfalls of Deep Learning - When Neural Networks are not the solution](https://www.wearedevelopers.com/videos/14-the-pitfalls-of-deep-learning-when-neural-networks-are-not-the-solution) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [7 Most Popular Web Developer Jobs in Europe](https://www.wearedevelopers.com/magazine/163-7-most-popular-web-developer-jobs-in-europe)