About This Role

At MindMeld, we build AI applications that customers really care about and that can be built today utilizing large scale machine learning and natural language understanding. Our Conversational AI platform enables us to build a high accuracy experience for a new domain in as little as 8 weeks. As a Machine Learning / NLP Engineer on our small, 27-person team, you will own several components and features of the MindMeld Conversational AI platform, as well as play a major role in one of several proof of concept or production deployments for Fortune 500 companies. You will join a team trying to achieve state-of-the-art end-to-end accuracy (>99%) for a large vocabulary knowledge domain and satisfy the long tail of user requests. You will primarily code in python and leverage a host of machine learning toolkits like scikit-learn, numpy, scipy, duckling etc. You will use crowdsourcing tools like Amazon Mechanical Turk extensively for data collection.

About You

You have a passion for and deep experience in artificial intelligence, machine learning and natural language processing. You thrive in an agile development process. You look forward to joining a high caliber team of machine learning and natural language processing experts. You’re not just looking for a job, you’re looking for an innovative product where you can make a large impact. You’re self-driven and set high expectations for yourself. You take ownership and sweat the small stuff. You thrive on constructive feedback. You insist that facts drive decisions. You deliver results that matter.

Things we look for:

  • B.S., M.S., or Ph.D. in Computer Science or Machine Learning
  • 3+ years of applied Machine Learning / NLP experience in the industry
  • Solid knowledge of statistical classifier models (HMM, SVM, deep/recurrent ANN, CRF, LMT, etc.) and of best practices in attribute selection, dimensionality reduction, runtime performance optimization
  • Familiarity with toolkits such as Scikit-learn, numpy, scipy, R, Weka, Matlab, NLTK, Stanford CoreNLP
  • Fluency in Python or other scripting languages
  • Knowledge of NLP techniques such as PoS tagging, NP chunking, shallow/deep parsing, NER
  • Knowledge of IR concepts such as statistical search ranking, knowledge graphs, vectorial semantics, LSA, document clustering
  • Strong communication skills
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