Research Scientist

This role is to work on Machine Learning research area Machine Learning engineers for applied research on Natural Language problem statements to create a new wave of human-assisted conversational AI technologies in the Curo Speech and Natural Language Processing team while working closely with a world-renowned team of speech, natural language and machine learning experts focused on driving invention and innovation in natural language technologies and services. Job Responsibilities

• Expertise in DNN Architectures {CNNs, LSTMs, Transformers} applied to Speech and Language Problems such as Question Answering, Summarization, Semantic Understanding
• Review published literature, conceptualize novel algorithms, implement, evaluate and facilitate deployment of solutions for speech, natural language and dialog problems encountered in human conversations and analytics.
• Develop rich models for specific tasks by collecting, curating, coordinating annotations of spoken conversations, training and adapting machine learning models
• Tune model performance through feature engineering to optimize model performance by combining rules and machine learning techniques
• Integrate the developed models into Curo software and deploy them on Interactions Platforms.
• Document work through conference publications, file patent disclosures.
• Mentor junior associates as required.

Qualifications Required

• MS or PhD degree in Computer Science/Statistics with experience in Machine Learning
• Proven success in applying Machine Learning models to practical problems
• Understanding of word & sentence representations like Word2Vec, Glove, Bert, ELMO etc
• Good understanding of pattern recognition algorithms like k-means, SVM, HMM, GMM, Neural
• Networks, Viterbi decoding etc
• Expertise in Python/C/C++
• Experience contributing to research efforts, including publishing in conferences

Preferred

• Experience working with machine learning tools, DNN tools, speech recognition tools, web crawlers, finite state machines, and open source natural language toolkits are a plus.
• Experience working with deep learning toolkits like PyTorch, Tensorflow etc.
• Experience in natural language processing technologies and services with emphasis in one or more of the following:
  • Data acquisition and NL modeling: harvesting NL resources from the Web, rapid bootstrapping of domain-specific and multilingual NL models for named-entity, syntactic parsing and text classification
  • NL systems: large-scale development and deployment, performance monitoring, tuning and optimization of NL models
  • NL methodology: grammar-based, data-driven and machine learning-based, hybrid approaches
  • NL technologies: spoken language understanding, language translation, natural language search, syntax-semantics.