This is an internship position in our New York office for the months of May-August 2019.
About Text IQ
TextIQ(www.textiq.com)is the category-leader in AI for sensitive information.Its technology protects organizations from high-stakes compliance, privacy, & legal disasters, with customers including Fortune 200 companies, government agencies, & tech giants, as well as leading healthcare, biotech, energy, insurance, & financial institutions. Using proprietary algorithms to efficiently & meticulously analyze big data,TextIQs software is capable of identifying sensitive, compromising, & privileged documents -- including those frequently overlooked by human reviewers.The company has been featured by Forbes (How AI Startup Text IQ Got Profitable By Shaving Millions Off Customers' Legal Costs) & was recently ranked among the 100 most promising AI companies in the world, with offices in San Francisco, New York City, Vancouver, & Dublin.
- Prototype & develop novel approaches to problems in NLP & Machine learning using techniques including deep learning, graphical models, & other paradigms of machine learning.
- Stay engaged with the NLP & ML research communities & keep up to date with relevant trends & developments. Attend academic & industry conferences.
- Collaborate with research & engineering teams working on a variety of NLP & machine learning problems.
- Design, experiment & evaluate models
- Write, test & maintain production-quality code
- MSc in ML or equivalent experience in related fields.
- Experience programming in Python, & C++ or Java.
- Expertise in graphical models & deep learning.
- Hands-on working experience with deep learning frameworks such as TensorFlow, Keras, PyTorch, Caffe, or MXNet.
- Proven track record of achieving results as demonstrated by grants, fellowships, patents, as well as first-authored publications at workshops or conferences such as ICML, NIPS, ACL, EMNLP, NAACL, or similar
- Special expertise in unsupervised, semi-supervised, & transfer learning, especially for unstructured text.
- Familiar with state of the art deep generative models (e.g. VAEs) for challenging problems with multiple modalities of data. Deep sequential models (e.g. RNNs). Semi-supervised & unsupervised learning using deep neural nets.
- Experience with cloud computing & machine learning on big data, particularly in deep learning. Experience with Spark or similar frameworks.