Senior Machine Learning Engineer

Quickbooks Payments platform one of Intuit's fastest growing business units, is looking for Sr. Software Engineers to design and develop innovative payments products and platform. If you are passionate about building world-class payments platform to delight customers, this is the opportunity for you! In this role, your responsibilities will include the following:

You will be directly responsible for the design and implementation of one or more subsystems within our payments platform. You will need to effectively collaborate with and influence other engineers, architects, and managers to solve complex problems. Comfortably move between understanding customer needs and solving customer problems through modifications of various levels of technical designs, implementation and testing. You will be involved in delivering functionality for requirements, high-level designs, influencing significant portions of the architecture, detailed designs and code implementation of features and supporting process improvement initiatives.

  • 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

    • 6 years of Machine Learning and core software/data engineerng experience
    • 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).
    • 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.
    • Mathematics fundamentals: linear algebra, calculus, probability

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