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# Staff Machine Learning Engineer

- **Company:** Intuit Inc.
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $197,000.0 - $266,500.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Application Integration Architecture, Cloud Computing, Nvidia CUDA, Data Structures, Github, R (Programming Language), Python (Programming Language), Machine Learning, NLTK (NLP Analysis), NumPy, Tensorflow, SQL Databases, Data Processing, Apache Spark, Keras, Git, Pandas, Scikit Learn, Information Technology, Production Code, Software Performance, Software Version Control
- **Published:** August 7, 2026
- **Apply:** https://www.careerboard.com/us/en/find-jobs-in-United-States/-D50ED22B512A85412B/

## About the Role

* BS, MS, or PhD degree in Computer Science or related field, or equivalent work experience.
 * 6+ years of experience
 * Knowledgeable with Data Science tools and frameworks (ie Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
 * Basic knowledge of machine learning techniques (ie classification, regression, and clustering).
 * Understand machine learning principles (training, validation, etc.)
 * Knowledge of data query and data processing tools (ie SQL)
 * Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (eg, I/O and memory tuning).
 * Software engineering fundamentals: version control systems (ie Git, Github) and workflows, and ability to write production-ready code.
 * Experience deploying highly scalable software supporting millions or more users
 * Experience with GPU acceleration (ie CUDA and cuDNN)
 * Experience with integrating applications and platforms with cloud technologies (ie AWS and GCP)
 * Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users

## Description

* 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.


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