Machine Learning Engineer

Summary

Join PENN Entertainment's Data Science & Machine Learning team as a Machine Learning Engineer to build and deploy sophisticated machine learning models impacting user experience across various platforms. You will design and implement personalized recommendation engines, chat toxicity detection systems, and cross-sell propensity models. The role involves optimizing machine learning pipelines, collaborating with cross-functional teams, and scaling the ML platform. You will contribute to high-impact projects and advance PENN's cutting-edge ML platform. This position offers opportunities for career growth and is a remote position.

Requirements

• 3+ years of professional experience as a Machine Learning Engineer or in a similar role
• A background in Computer Science, Data Science, Engineering, or a related technical field
• Strong programming skills in Python and SQL
• Experience with Docker, Kubernetes, Terraform, and scalable deployment tools
• Hands-on experience building CI/CD pipelines for ML systems
• Proficiency in orchestration tools like Airflow, Kubeflow, or Dagster
• Experience working on or contributing to dbt projects
• Comfort working in cloud environments like AWS, GCP, or Azure
• Familiarity with ML frameworks such as PyTorch, TensorFlow, Keras, or similar

Responsibilities

• 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

Preferred Qualifications

• Bonus for Go, Rust, Scala, R, or C++
• Experience building real-time personalization or recommendation systems at scale
• Familiarity with virtual feature stores like Feast or Featureform
• Exposure to working with or deploying large language models (LLMs) in production

Benefits

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