Senior Machine Learning Engineer

About Taskrabbit:

Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more.

At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love. Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world.

Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed!

This role operates on a hybrid schedule requiring two days of in-office collaboration per week. The position can be based in either our San Francisco office or our new New York City office (opening January 2026).

About the Role

Machine Learning is a cornerstone at Taskrabbit, and we're looking for a seasoned Senior Machine Learning Engineer to join our team and help shape the future of ML/AI at Taskrabbit. This is a unique, full-stack role for an individual who is passionate about the entire machine learning lifecycle—from initial research and model development to building the robust infrastructure required to deploy and scale your work.

As a Senior Machine Learning Engineer, you will tackle exciting challenges that directly impact how people discover and connect with home services on the Taskrabbit platform. You will play a crucial role in advancing our capabilities in areas like search ranking, content discovery, and recommender systems. You will collaborate closely with data scientists and other engineers to design and implement novel algorithms, and you will partner with software engineers to ensure the scalability, reliability, and optimization of our models in production.

What You'll Work On:

• Model Development & Research: Research, design, and implement machine learning models to solve key business problems in areas like search ranking, recommendations, and content discovery.
• End-to-End ML Lifecycle: Own the entire lifecycle of ML models, including feature engineering, training, evaluation, deployment, and monitoring.
• Infrastructure & Scalability: Build scalable and reliable ML infrastructure and data pipelines that support reproducible feature engineering and machine learning model deployment in real-time, near real-time, and batch processes.
• Performance & Quality: Build monitoring services to understand data quality and model performance of complex systems, and collaborate with engineering and science teams to optimize existing algorithms for training and evaluation.
• Software Engineering Excellence: Independently solve complex problems, write clean, efficient, and sustainable code, and actively participate in code reviews, documentation, and 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.
• 3+ 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 areas like search, ranking, recommender systems, or NLP.
• 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 (Data Build Tool) is a plus for transforming and testing data.
• Familiarity with tools for Infrastructure as Code, such as Terraform, and CI/CD pipelines.
• Excellent communication skills