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Machine Learning/ML Engineer

Roles & Responsibilities

  • 5+ years of industry experience in applied machine learning, or a PhD with at least 2 years of ML experience
  • Strong programming skills in Python and one of Java or Scala
  • Experience with distributed data processing tools such as Hive, Spark, and Airflow, and ML frameworks like TensorFlow or PyTorch
  • Production experience in developing and deploying machine learning models; familiarity with Docker and Kubernetes is a plus

Requirements:

  • Develop, train, evaluate, and productionize machine learning models for production systems
  • Build and maintain end-to-end ML infrastructure and data pipelines in collaboration with data engineers
  • Leverage distributed data processing tools (Hive, Spark, Airflow) and ML frameworks (TensorFlow or PyTorch) for model development and deployment
  • Deploy ML services using Docker and Kubernetes, ensuring scalable, reliable serving and monitoring

Job description


 Job Name        : Machine Learning/ ClientEngineer
Job Location   : Remote, Can be Anywhere, Preferably in West Coast or someone who can work in PST
Job Type          : Contract - 6 months
Client               : Airbnb
Rate                     : Depend on Experience

Job Authorization : US Citizen/ GC /EAD (H4/L2/TN) preferred C2c are open only for own corp, not accepted from any employers


Job Description: 
  •  5+ years of industry experience or a PhD + 2 years industry experience in applied Machine learning.
  • Strong coding skills in Python/Java/Scala or equivalent
  • Experience with distributed data processing tools like Hive, Spark, Airflow and popular Client frameworks like Tensorflow or Pytorch is preferred
  • Solid understanding of engineering best practices and complexities of models in production
  • Experience developing and productionizing machine learning models is a plus
  • Experience with Docker, Kubernetes, Spark is a plus
  • Experience with distributed data processing tools like Hive, Spark, Airflow is a plus
  • Industry experience building end-to-end Machine Learning Infrastructure a bigplus

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