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Machine Learning Engineer 2

Category Software Engineering Location Mountain View, California; San Diego, California; New York, New York; Atlanta, Georgia Job ID 2024-55675

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Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job 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...

Responsibilities

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.

Qualifications

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.

Qualifications

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).

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Intuit provides a competitive compensation package with a strong pay for performance rewards approach. The expected base pay range for this position is 132,000 – 178,500, Bay Area California $136,00 – 184,000, Southern California $122,500 – 165,500 . This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing pay equity for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

Career Path

Mobility plays an important role for our engineering community. In addition to vertical growth, our teams offer lateral growth opportunities. Whether it’s working on a different tech stack or product, Intuit will help you get to the next step in your career.