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 your career growth and offers various benefits. If you want to challenge conventions in gaming, media, and entertainment, we want to talk to you.

Requirements

• Experience: 5+ years of experience in machine learning engineering, data engineering, or backend software engineering, with demonstrated experience building ML systems in production
• Technical Skills: Proficiency in Python and SQL. Deep familiarity with cloud platforms such as GCP, AWS, or Azure
• MLOps & Infrastructure: Hands-on experience with ML model deployment, CI/CD pipelines, containerization (Docker, Kubernetes), and orchestration tools (Dagster, Airflow, Kubeflow, or similar)
• ML Tooling: Experience with model packaging and serving technologies such as MLflow, Seldon, Vertex AI, or AWS SageMaker
• Collaboration: Strong communication skills and a desire to work cross-functionally with data scientists, ML engineers, and platform teams
• Education: 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