What you will do:

Collaborate with our applied scientists to accelerate the creation and development of machine learning product features Create fast and reliable pipelines to ingest, explore, transform, featurize and segregate machine learning data Support the development of machine learning-driven natural language processing systems for question-answering, summarization, discourse segmentation, text classification, entity-resolution and topic analysis Design and build systems to train, evaluate, and deploy machine learning models online and offline Optimize and scale machine learning models in production

Minimal qualifications:

Significant experience supporting machine learning experimentation and commercialization Solid understanding of data engineering best practices (batch data processing, stream data processing, etc.) Considerable experience with distributed systems and map-reduce based technologies, especially Spark Substantial experience working with both SQL and NoSQL databases and ElasticSearch Proficient with other data engineering tools like Airflow, Luigi, and Hive. Excellent software engineering skills, e.g., design, unit testing, code reviews, etc. Experience using Amazon Web Services and Google Cloud for production data pipelines Expert skills in Python and Java Preferred qualifications: Experience building production pipelines for ML-driven NLP systems at scale.