Responsibilities

• Propose and implement a scalable ML modeling workflow for standardized use by our data scientists
• Enhance and improve existing model infrastructure to support the growing demands of the data science organization
• Develop internal tools to enable seamless configuration, fitting, and quality assurance of various classes of machine learning models
• Collaborate closely with data science, engineering, and product to ensure infrastructure meets the needs of internal and external stakeholders

Requirements

• Advanced degree in a quantitative discipline or compensating work experience
• 5 or more years of machine learning engineering experience, including working with data scientists, constructing machine learning models and developing production-level code
• Knowledge of various machine learning model structures, model performance metrics, and data visualizations for QA, including underlying mechanisms and use cases/tradeoffs
• Expert knowledge of Python & SQL
• Experience in Kubernetes, Airflow, Spark, Dataflow, and Postgres is a plus
• Demonstrated experience with cloud computing, git, and software architecture
• Collaborative, team-focused attitude with a strong desire to advance the product and learn new things
• Self-starter with experience turning ambiguous ideas into actionable designs
• Excellent written and verbal communication
• Eligible to work within the United States