Machine Learning Engineer II

Chewy Chewy · Retail · Boston, MA

Machine Learning Engineer II at Chewy focused on applying AI/ML to improve big data and predictive applications. Responsibilities include designing, testing, and scaling frameworks, systems, and models, researching and implementing ML algorithms, and developing ML applications from design to deployment. Requires a Master's degree with 2 years of experience or a Ph.D. with 1 year of experience in a relevant quantitative field, with experience in AWS tools, Databricks, R, PySpark, PyTorch, TensorFlow, Docker, and various ML techniques.

What you'd actually do

  1. Apply artificial intelligence and/or machine learning methods to design, test and scale effective, reliable frameworks, systems, and models to improve the usefulness of big data and predictive applications.
  2. Research, implement, and test machine learning algorithms and tools to solve data challenges and to improve quality and reliability of data.
  3. Develop machine learning applications according to requirements; design, code, test, deploy and iterate on machine learning systems.
  4. Research and remain current on machine learning and optimization techniques.
  5. Work with data scientists, application developers, product managers and software engineers to develop and support software for new machine learning products.

Skills

Required

  • Master's degree in Industrial Engineering, Operations Research, Statistics, Applied Mathematics, or related field of study and 2 years of experience required as an Operations Research Analysts or a related position/occupation.
  • Ph.D.’s degree in Industrial Engineering, Operations Research, Statistics, Applied Mathematics, or related field of study and 1 year of experience required as an Operations Research Analysts or a related position/occupation.
  • Amazon Web Services tools like Redshift, SageMaker, Snowflake or other similar platforms, Databricks.
  • R, PySpark, PyTorch, TensorFlow, Docker.
  • ML techniques like neural networks, linear regression, Bayesian statistics and XG boost models.
  • Developing and deploying optimization, predictive and machine learning model.

What the JD emphasized

  • design, test and scale effective, reliable frameworks, systems, and models
  • develop and deploy optimization, predictive and machine learning model

Other signals

  • develop and deploy machine learning models
  • improve usefulness of big data and predictive applications
  • research, implement, and test machine learning algorithms