About Location Team Publications Join Us
An NYC-Based Startup Building
The Future Of Search.
Join Our Team
Combining structured and unstructured knowledge buried in volumes of text, databases and internal external repositories.
Open Positions:   Research Scientist/Engineer
Consolidating knowledge and helping business clients make sense of their data.
Open Positions:   Research Scientist/Engineer
Designing and building robust, large scale systems with user experience in mind.
Open Positions:   Front-end, Back-end Engineers
Discover Us
About Laer
We are an early stage startup currently operating in "stealth mode". We are driven by a singular vision of radically transforming the experience of search and helping organizations find meaning and patterns hidden behind volumes of disparate and unstructured data.
NYC-Based
Our Location
We are currently located on a beautiful new Cornell University campus on Roosevelt Island, in the center of New York City.
Meet Us
The Team
Igor Labutov
PhD, Cornell, Carnegie Mellon
Igor received his PhD in Computer Engineering from Cornell University, and was a postdoc at the Machine Learning Department at Carnegie Mellon University, where he built conversational agents that learn from natural language interactions with users.
Bishan Yang
PhD, Cornell, Carnegie Mellon
Bishan received her PhD in Computer Science from Cornell University, and was a postdoc at the Machine Learning Department at Carnegie Mellon University, where she built machine learning systems that make sense of text documents.
Brian Lu
Computer Science, Carnegie Mellon
Brian is an undergraduate in Computer Science from Carnegie Mellon University. His main research interests are in machine learning and natural language processing.
Join Us!
We Are Hiring
We are looking for driven and ambitious individuals who will be at the forefront of driving the revolution of enterprise search. We are also offering Co-op and internship opportunities.
Back-end Engineer

Description:
You will be envisioning, designing and building resilient and scalable software infrastructure for delivering industrial-strength artificial intelligence applications.


Requirements:

- Experience with solving large-scale real-world system problems.
- Strong ability to develop production software with clean and efficient code.
- Experience with industrial database systems (e.g., HBase, PostgreSQL, MongoDB) strongly preferred.



Apply Now
Research Scientist

Description:
You will be envisioning, designing and building innovative solutions for real world natural language processing problems.


Requirements:
- Familiar with basic machine learning and natural language processing algorithms.
- Experience with developing fast and accurate algorithms for solving practical machine learning problems.
- Experience with Question Answering, Information Extraction, and Knowledge Base Construction strongly preferred.



Apply Now
Front-end Engineer

Description:
You will be envisioning, designing and building beautiful and intuitive user interface and web applications.


Requirements:
- Experience with developing large real-time web applications (e.g., React, Node.js, Angular).
- Work closely with clients and integrate their feedback into product design.
- Experience with UI/UX design strongly preferred.



Apply Now
Discover Knowledge
Relevant Publications


"Learning Semantic Parsers from Natural Language Supervision"
Igor Labutov, Bishan Yang, and Tom Mitchell. EMNLP 2018.
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"Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds"
Igor Labutov, Bishan Yang, Anusha Prakash, and Amos Azaria. ACL 2018.
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"Zero-shot Learning of Classifiers from Natural Language Quantification"
Shashank Srivastava, Igor Labutov, and Tom Mitchell. ACL 2018.
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"Leveraging knowledge bases in LSTMs for improving machine reading."
Bishan Yang and Tom Mitchell. ACL 2017.
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"A Joint Sequential and Relational Model for Frame-Semantic Parsing."
Bishan Yang and Tom Mitchell. EMNLP 2017.
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"End-to-End Learning for Structured Prediction Energy Networks."
David Belanger, Bishan Yang, and Andrew McCallum. ICML 2017.
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"Embedding entities and relations for learning and inference in knowledge bases."
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. ICLR 2015.
View Publication
About Location Team Publications Join Us
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