Machine Learning Engineer- Cloud Deployment
2 months ago
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.
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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