Intuit Staff Machine Learning Engineer in San Diego, California
Intuit is hiring a Staff Machine Learning Engineer to focus on our Consumer Tax Group. We are looking for exceptional talent that can improve the bottom lines of our TurboTax offerings. Our Senior Machine Learning Engineer will focus on Consumer Tax Group products, and will work closely with Data Scientists to deliver new data-driven technologies and personalize the customer experience in our flagship TurboTax products
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..
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 we wish to deliver.
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
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
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