Staff Machine Learning Engineer Job in Ice Mortgage Technology

Job Purpose We are looking for a Staff Machine Learning Engineer that will help us discover the information hidden in vast amounts of data and documents and help us make smarter decisions to deliver even better products. You will have strong Deep Learning and Python programming skills. Your primary focus will be in applying computer vision and NLP techniques on large number of documents, doing statistical analysis, and building high quality prediction systems integrated with our products. In that role, you will work closely with product management as well as application development teams. Responsibilities Participate in discovery processes collaborating with key stakeholders to identify business requirements and expected outcomes. Develop Neural Network models for information extraction from mortgage documents using Computer Vision and NLP techniques. Design and build scalable ML pipelines to serve Machine Learning models at scale. Design experiments, test hypotheses, and build models. Conduct advanced data analysis and highly complex designs algorithm. Perform ad-hoc analysis and present results in a clear manner to a wide audience and key stake holders. Delivers high-quality software based on best practices for data collection, modeling, and software development. Collaborate with development teams on deploying models to production environment to support ML-driven product features. Define business-specific performance metrics to measure model effectiveness. Monitor and improve metrics over time for models used in production environment. Knowledge and Experience B.S. in mathematics, statistics or computer science or related field; M.S or Ph.D. degree preferred. Experience in building production-grade Neural Network models using CV or NLP techniques. 6 or more years of relevant ML engineering experience in building highly scalable ML pipelines using distributed data processing, and distributed deep learning techniques. Hands-on programming experience in Python, Spark programming, Java, or other programming languages. Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, Neural Networks, etc. Experience with Deep Learning frameworks, such as TensorFlow, PyTorch, MxNet. Proficiency in using query languages such as SQL, Hive, Pig and NoSQL databases. Great communication skills and ability to convey complex topics to a cross-functional audience.