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iRobot Corp.


Bedford, MA

www.irobot.com

Jobs

Data Engineering Intern

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Posted on: March 5, 2021 Apply Now
Bedford, MA Co-op Expires May 12, 2021

Introduction

Are you excited by the idea of making robots that are loved by millions of people all over the world? As a Data Engineering Intern on the MLOps (Machine Learning Operations) team you will have an important hand in helping to ensure that our robots are operating at peak performance in the consumers’ homes. Come join a fantastic team of engineers at iRobot building data pipelines, tools and infrastructure used to develop the machine learning models that make our robots smart. As a pioneer in the robot industry, our goal is to drive innovation, serve as an industry catalyst and change the world by empowering people to do more.What you will do:

Collaborate with machine learning engineers and researchers to enable faster development of ML models that run embedded on our robots.
Work with state-of-the-art tools and frameworks to build scalable and efficient solutions for data management, processing and dataset building.
Create essential plumbing in the AWS cloud such as CI/CD for software applications and the compute infrastructure needed to develop ML models.
Help with infrastructure supporting data collection and data annotation efforts.

Qualifications:

  • Enrolled full-time as a student majoring in an applicable field and returning to university/college for at least a term, semester or quarter following your internship period
  • Strong Python object-oriented software development skills
  • Experience with git and relational databases (e.g., PostgreSQL, MySQL)
  • Good communication skills

Bonus Qualifications:

  • Software and/or DevOps skills with public clouds (AWS, Azure, or GCP; AWS preferred)
  • Experience building data collection, storage and processing pipelines
  • Experience with Django or other web frameworks
  • Exposure to big data concepts and distributed computing frameworks (e.g., Kubernetes, Spark, Hadoop)
  • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
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