Senior Machine Learning Engineer, Platform

About the Role & Team 
The Machine Learning Platform team at Penn Entertainment builds the infrastructure, tools, and frameworks that power our machine learning lifecycle—from training and deployment to monitoring and optimization. As a Machine Learning Engineer, you’ll play a key role in scaling and evolving our ML platform. You’ll work closely with data scientists, ML engineers, and data engineers to design robust, efficient, and production-grade systems that accelerate ML innovation across the company. 

This is a hands-on engineering role focused on creating the foundational systems that support the development, deployment, and operation of machine learning models at scale. 

About the Work  

• 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.

About You 

• 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.

Nice to have 

• 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.

What We Offer : 

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