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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