Senior Machine Learning Engineer

About the role

This is a unique, full-stack role for an individual passionate about the entire machine learning lifecycle—from initial research and model development to building robust infrastructure.

Responsibilities

• 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.

Requirements

• BS, MS, or PhD in Computer Science, Statistics, Operations Research, or a related quantitative field.
• 5+ 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.
• 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, 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.

Qualifications

• BS, MS, or PhD in Computer Science, Statistics, Operations Research, or a related quantitative field.
• 5+ 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.
• 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, 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.

Skills

• Programming Languages: Python, Java, C++, etc.
• Machine Learning Libraries: Scikit-learn, lightgbm, xgboost, TensorFlow, PyTorch, etc.
• Modern Data and ML Technologies: Docker, Kubernetes, Kafka, Airflow, data warehouses (eg snowflake, redshift or BigQuery), and data lakes.
• Tools for Infrastructure as Code: Terraform, CI/CD pipelines.
• Communication Skills: Clear presentation of complex findings and recommendations to both technical and non-technical audiences.
• Technological Learning: Quick learning of new technologies and solving challenging problems.

Benefits

• Employer-paid health insurance
• 401k match with immediate vesting
• Taskrabbit product stipends
• Wellness + productivity + education stipends
• IKEA discounts
• Reproductive health support

Pay

The base pay range for this position is $150,000 - $200,000. This range is representative of base pay only, and does not include any other total cash compensation amounts, such as company bonus or benefits. Final offer amounts may vary from the amounts listed above and will be determined by factors including, but not limited to, relevant experience, qualifications, geography, and level.

Schedule

Taskrabbit is a Hybrid Company. We value flexibility and choice but also stay committed to regular in-person connection.