Staff Machine Learning Engineer, Intuit

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, 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

Requirements & Skills:

• BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
• 8+ years of 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. recommender systems, 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 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)
• Strong oral and written communication skills. Ability to conduct meetings make professional presentations, and explain complex concepts and technical material to non-technical users