Senior Software Engineer, ML Platform

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 the 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 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 Engineering team is responsible for building, deploying, and optimizing models and APIs to help improve all of Penn Entertainments digital offerings. Our team values creativity, collaboration, ingenuity, and ownership. As a Senior Software Engineer, ML Platform you'll be instrumental in crafting the next generation of user experiences. You will design, build, and deploy sophisticated machine learning models and infrastructure that directly impact how users discover content, engage with our community, and explore the full spectrum of offerings. This role offers a unique chance to contribute to high-impact projects while helping to advance our cutting-edge ML platform.

About the Work

We're focused on projects that directly improve user engagement and satisfaction. Some examples include:

• Personalized Recommendation Engines: Connect users with the content, games, and promotions they'll love.
• Experimentation Framework: Guide data-driven decision-making by providing foundations for AB testing and experimentation.
• Dynamic Personalization: Implement real-time, ML-driven decisions to create seamless user journeys.
• Cutting-Edge MLOps: Help us scale our ML platform using the latest tools and best practices (GCP, Kubernetes, PyTorch, Dagster, and more).

As part of the Machine Learning Engineering team, you will:

• Build and optimize end-to-end machine learning pipelines from data ingestion to deployment.
• Work closely with Product, Marketing, and Operations teams to align ML solutions with business goals.
• Improve our ML platform and deploy infrastructure using MLOps best practices.
• Evaluate and integrate new tools, models, and frameworks to enhance scalability and performance.
• Clearly communicate technical concepts to both technical and non-technical stakeholders.
• Document your systems and workflows using Git, Confluence, and related tools.

About You

You’re someone who’s passionate about putting machine learning into production and making personalization work at scale. You bring:

• Experience: 5+ years of experience in ML, data, distributed streaming, and backend systems software engineering, with demonstrated experience building ML systems in production.
• Technical Skills: Extensive 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, IaC tools (Terraform), containerization (Docker, Kubernetes), and orchestration tools (Dagster, Airflow, Kubeflow, or similar).
• ML Tooling: Experience with model packaging and serving technologies such as TensorFlow, Pytorch, MLflow, 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:

• Significant experience building real-time personalization and/or recommendation systems at scale.
• Extensive familiarity with monitoring, observability, and alerting tools for scalable distributed ML systems.
• Exposure to working with or deploying large language models (LLMs) in production.

What We Offer

• Competitive compensation package
• Fun, relaxed work environment
• Education and conference reimbursements.
• Opportunities for career progression and mentoring others

#LI-REMOTE