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Staff Machine Learning Engineer 341 views

Intuit is looking for innovative and hands-on machine learning engineers to help the central data science team develop, design and integrate mathematical models into production. Their team builds AI/ML solutions for all the internal teams within Intuit like Engineering, HR, Finance & Legal. They are looking for team members that love new challenges, cracking tough problems and working cross-functionally. If you are looking to join a fast-paced, innovative and incredibly fun team, then they encourage you to apply. Come do the best work of your life!

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.

What you’ll bring

  • BS, MS, or Ph.D. 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.

Preferred Additional Qualifications:

  • Interest in reading academic papers and trying to implement state-of-the-art experimental systems.
  • Experience using deep learning architectures.
  • Experience deploying highly scalable software supporting millions or more users.
  • Experience with GPU acceleration (i.e. CUDA and cuDNN).
  • Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP).
  • Experience with NLP and NLU.

How you will lead

  • 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, and 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 they wish to deliver

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