Machine Learning Infrastructure Engineer

ipsy   •  

San Mateo, CA

Not Specified years

Posted 235 days ago

This job is no longer available.


ipsy is an innovative fast-growing beauty company headquartered in San Mateo, CA. We are looking for a Machine Learning Infrastructure Engineer to join our personalization team. You will be developing infrastructure used to train and deploy custom machine learning models.  You will work with our comprehensive data sets and have a direct impact on our growing member base of several million. The ideal candidate is hands-on and thrives in a fluid, high-performance entrepreneurial environment with a passion for learning and innovating.

Must-Have Qualifications:

  • Successful track record of deploying machine learned models into real-world settings
  • Experience scaling algorithms to handle large production datasets
  • Experience with ETL and big data tools, e.g. Hadoop or Spark
  • Working knowledge of open source machine learning libraries, e.g. Scikit-learn, MLlib, TensorFlow, etc.
  • Knowledge of supervised and unsupervised machine learning methods
  • Bachelor's in Computer Science, Computer Engineering, Mathematics, or equivalent field
  • Strong overall programming ability
  • Strong communication skills

About us:

ipsy was founded with one singular mission: to inspire individuals around the world to express their unique beauty. That’s how the ipsy Glam Bag came to life. With five products personalized for you—plus articles, videos, and more on—you’re free to try new things and express who you are. ipsy Shopper takes our mission to the next level by making beauty more accessible, rewarding, and liberating than ever before. And with ipsy Gen Beauty, Open Studios, and all the creator content that we post each day, our intention has remained the same: to give every individual the tools to form their own definition of beauty. With over 2.5M members and 8K digital content creators in our community, and generating over half a billion monthly content views, we’re just getting started.