Machine Learning Engineer

Machine Learning Engineer

Location: Hybrid 3 days in office flexible work hours

Overview

We are seeking a Machine Learning Engineer to join a collaborative engineering team focused on building data-driven insights for consumer engagement and commerce. This role will work closely with a cross-functional pod of engineers specializing in data analysis, data pipelines, and product development.

The successful candidate will contribute to the development and maintenance of machine learning pipelines and analytics systems that generate insights from large industry datasets. These insights help organizations better understand customer behavior and optimize strategies for selling tickets, merchandise, and other fan-related products.

This position offers an excellent opportunity for a junior to intermediate ML Engineer or Data Engineer who enjoys working with real-world data, building machine learning pipelines, and collaborating in a fast-paced environment.

Key Responsibilities

• Design, develop, and maintain machine learning pipelines and data workflows.
• Perform feature engineering and data preparation to support model development.
• Build and operate machine learning pipelines using AWS services such as SageMaker.
• Gather and analyze industry-wide datasets to generate actionable insights.
• Develop machine learning models to support analytics across multiple domains.
• Collaborate with data analysts, engineers, and product team members within a cross-functional pod.
• Conduct exploratory data analysis and write SQL queries to extract and analyze data.
• Monitor model performance and support continuous improvements to ML workflows.
• Document processes, pipelines, and model outputs to support team collaboration.

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• 2–5 years of experience in machine learning engineering, data engineering, or data science roles.
• Strong understanding of machine learning concepts and feature engineering.
• Experience building or maintaining machine learning pipelines.
• Experience working with AWS cloud services, ideally including SageMaker.
• Strong SQL skills and experience performing data analysis.
• Ability to work with large datasets and transform data into meaningful insights.
• Strong problem-solving ability and curiosity about data-driven systems.

Preferred Qualifications

• Experience with sports, sports marketing, or consumer analytics.
• Experience working in startup or fast-paced environments.
• Familiarity with data pipelines and analytics systems.
• Exposure to Python-based ML tools or frameworks.