Senior Machine Learning Engineer, Platform

Penn Interactive

About the position

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.

Requirements

  • 5+ years of experience in machine learning engineering, data engineering, or backend software engineering, with demonstrated experience building ML systems in production.
  • Proficiency in Python and SQL.
  • Deep familiarity with cloud platforms such as GCP, AWS, or Azure.
  • Hands-on experience with ML model deployment, CI/CD pipelines, containerization (Docker, Kubernetes), and orchestration tools (Dagster, Airflow, Kubeflow, or similar).
  • Experience with model packaging and serving technologies such as MLflow, Seldon, Vertex AI, or AWS SageMaker.
  • Strong communication skills and a desire to work cross-functionally with data scientists, ML engineers, and platform teams.
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.

Nice-to-haves

  • 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

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