Tech Stack

Description

You will bridge the gap between research and production-ready ML systems, directly impacting how millions of fans discover and purchase tickets, optimize pricing and inventory, personalize experiences, and prevent fraud. You'll design and build ML infrastructure and services at scale, working closely with data scientists, PMs, and software engineers.

Requirements

• 4+ years software engineering with 2+ years focused on ML systems and MLOps
• Strong programming skills in Python and experience with ML frameworks like scikit-learn, TensorFlow, PyTorch
• Experience with cloud platforms (AWS) and containerization technologies
• Understanding of batch and real-time ML systems, including model serving, A/B testing, and performance monitoring
• Product mindset and commitment to team collaboration

Responsibilities

• Design, build, and deploy machine learning models and systems at scale in production
• Build and maintain ML infrastructure including feature stores, model serving platforms, and real-time inference pipelines
• Embed on a product engineering team and collaborate with data scientists, PMs, and software engineers to productionize models
• Solve complex technical challenges in real-time pricing optimization, demand forecasting, and fraud detection
• Develop automated ML pipelines for training, validation, deployment, and monitoring using MLOps best practices