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Staff Machine Learning Engineer

Intuit

$197,000 - $266,500
Oct 24, 2025
Mountain View, CA, US
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Intuit is looking to solve the problem of conceiving, coding, and deploying data science models at scale using the latest industry tools

Requirements

  • 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)
  • Understand machine learning principles (training, validation, etc.)
  • Knowledge of data query and data processing tools (i.e. SQL)
  • Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning)
  • Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code
  • Experience with GPU acceleration (i.e. CUDA and cuDNN)

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
  • Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver
  • Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders
  • Deploy highly scalable software supporting millions or more users

Other

  • BS, MS, or PhD degree in Computer Science or related field, or equivalent work experience
  • 6+ years of experience
  • 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
  • Ability to work cross functionally with product managers, data scientists and product engineers
  • Travel requirements not specified