PENN Entertainment, Inc. is North America’s leading provider of integrated entertainment, sports content, and casino gaming experiences. From casinos and racetracks to online gaming, sports betting and entertainment content, we deliver the experiences people want, how and where they want them.
We’re always on the lookout for those who are passionate about creating and delivering cutting-edge online gaming and sports media products. Whether it’s through ESPN BET, Hollywood Casino, theScore Bet Sportsbook & Casino, or theScore media app, we’re excited to push the boundaries of what’s possible. These state-of-the-art platforms are powered by proprietary in-house technology, a key component of PENN’s omnichannel gaming and entertainment strategy.
When you join PENN Entertainment’s digital team, you’ll not only work on these cutting-edge platforms through theScore and PENN Interactive, but you’ll also be part of a company that truly cares about your career growth. We’re committed to supporting you as you expand your skills and explore new opportunities.
With locations throughout North America, you can build a future at PENN Entertainment wherever you are. If you want to challenge conventions in gaming, media and entertainment, we want to talk to you.
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
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