Machine Learning Engineer (ESPN Bet)

About the Role & Team

The Data Science & Machine Learning team is responsible for building models and APIs to help improve all of Penn Entertainments digital offerings. Our team values creativity, collaboration, ingenuity, and ownership.  As a machine learning engineer, you will get the opportunity to contribute to, optimize, and deploy many exciting models as well as help the team build net-new features into our machine learning platform.

Examples of some of our on-going projects:

• Recommendation engines: we want to direct users to content they want to see.
• Chat-Toxicity Modelling: create an inclusive community chat environment.
• Cross-sell Likelihood: enable users to access the full range of Penn
  Entertainment's offerings.
• Bot User Identification: fight fraud on Penn Entertainment’s digital offerings by
  identifying non-human users

About the Work

As a key member of our Machine Learning Engineering team, you will:

• Design and build new machine learning pipelines and optimization routines.
• Deploy modes and deliverables in conjunction with functional team leaders and
  stakeholders (in Product, Operations, Marketing, etc.)
• Improve our machine learning platform by implementing ML ops best practices.
• Conduct thorough testing and evaluation of new tools and technologies to
  assess their suitability for our platform.
• Communicate clearly and efficiently with technical and non-
  technical stakeholders.
• Write and maintain technical design and git/confluence documentation.
• Other duties as required

About You

• A minimum of 5 years of professional experience, 3 as a Machine Learning Engineer
• A degree/background in Computer Science, Data Science, Statistics, Computer
  Engineering, or a related technical field.
• Extensive experience in deploying applications using Docker, Kubernetes,
  Terraform, GitHub and other relevant tools.
• Proficient with Python and SQL. Languages like Go, Rust, Scala, R, and C++ are
  nice-to-have.
• Proven expertise in setting up Continuous Integration/Continuous Deployment
  (CI/CD) pipelines for Machine Learning projects. Skilled in testing and validating
  code, data, data schemas, and models.
• Demonstrated experience developing machine learning pipelines with
  orchestration tools like Airflow, Kubeflow, or Dagster.
• Extensive experience building and/or contributing to dbt projects.
• Experience developing and deploying machine learning solutions in a public
  cloud such as AWS, Azure, or Google Cloud Platform is preferred.
• Familiarity with popular machine learning frameworks such as TensorFlow,
  PyTorch, Caffe, and/or Keras

Nice to Have

• Experience building real-time stream processing solutions with technologies such
  as Kafka, Spark, and Flink.
• Experience with virtual feature store technologies such as Featureform or Feast.
• Experience integrating with BI tools such as Mode, Tableau, Looker, or
• Background in deploying and monitoring large language models (LLMs).

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

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