Intuit

Machine Learning Engineer 2

Job description

Overview

In this role, you’ll be embedded inside a vibrant team of data scientists. You’ll be expected to help conceive, code, and deploy data science models at scale using the latest industry tools. Important skills include data wrangling, feature engineering, developing models, and testing metrics. You can expect to...

  • Discover data sources, get access to them, import them, clean them up, and make them “machine learning ready”.
  • Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
  • Partner with data scientists to understand, implement, refine and design machine learning and other algorithms.
  • Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models.
  • Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders.
  • Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.

What you'll bring

  • BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
  • Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
  • Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering).

How you will lead

  • Model Prototyping: The ML Engineer would be expected to build prototype models alongside data scientists. This may involve data exploration, high-performance data processing, and machine learning algorithm exploration. The ML engineer will be expected to come up with a rationale for model choice and come up with metrics to properly evaluate models.
  • Model Productionalization: Works with data scientists to productionalize prototype models to the point where it can be used by customers at scale. This might involve increasing the amount of data used to train the model, automation of training and prediction, and orchestration of data for continuous prediction. The engineer would be expected to understand the details of the data being used and provide metrics to compare models.
  • Model Enhancement: Work on existing codebases to either enhance model prediction performance or to reduce training time. In this use case you will need to understand the specifics of the algorithm implementation in order to enhance it. This enhancement could be exploratory work based off of a performance need or directed work based off of ideas that other data science team members propose.
  • Machine Learning Tools: The ML Engineer would build a tool for a specific project, or multiple projects though generally these types of projects are decoupled from any one project. The goal of this type of use case would be to ease a pain point in the data science process. This may involve speeding up training, making a data processing easier, or data management tooling.

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