Senior Machine Learning Engineer, Platform

Summary

Join PENN Entertainment's digital team as a Machine Learning Engineer and build the infrastructure, tools, and frameworks that power our machine learning lifecycle. You will play a key role in scaling and evolving our ML platform, working closely with data scientists, ML engineers, and data engineers. This hands-on engineering role focuses on creating foundational systems supporting the development, deployment, and operation of machine learning models at scale. PENN Entertainment offers a competitive compensation package, a fun work environment, education and conference reimbursements, parental leave top-up, and opportunities for career progression. The company is committed to supporting your career growth and offers various benefits. PENN Entertainment is an equal opportunity workplace.

Requirements

• 5+ years of experience in machine learning engineering, data engineering, or backend software engineering, with demonstrated experience building ML systems in production
• Proficiency in Python and SQL
• Deep familiarity with cloud platforms such as GCP, AWS, or Azure
• Hands-on experience with ML model deployment, CI/CD pipelines, containerization (Docker, Kubernetes), and orchestration tools (Dagster, Airflow, Kubeflow, or similar)
• Experience with model packaging and serving technologies such as MLflow, Seldon, Vertex AI, or AWS SageMaker
• Strong communication skills and a desire to work cross-functionally with data scientists, ML engineers, and platform teams
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field

Responsibilities

• Design, build, and maintain core components of the ML platform including model serving infrastructure, feature stores, and monitoring systems
• Develop and maintain CI/CD pipelines for ML workflows to support reproducibility, scalability, and continuous delivery of models
• Collaborate with ML engineers and data scientists to support model experimentation, packaging, and deployment in both batch and real-time contexts
• Contribute to the development of best practices for MLOps, including versioning, lineage tracking, observability, and governance
• Write clean, testable, and well-documented code and contribute to team knowledge through documentation and design reviews
• Partner with data engineering and platform teams to ensure seamless integration with data pipelines and compute environments

Preferred Qualifications

• Exposure to large language models (LLMs) and their deployment considerations
• Familiarity with monitoring, observability, and alerting tools for ML systems
• Contributions to open-source MLOps tooling or platforms

Benefits

• Competitive compensation package
• Fun, relaxed work environment
• Education and conference reimbursements
• Parental leave top up
• Opportunities for career progression and mentoring others