
Analytical Linguists work across Google to drive improvements in quality, classification, information structure, and natural language understanding and generation. As an Analytical Linguist you will work both on complex projects spanning multiple products, groups, and disciplines, and on tightly focused efforts to produce specific product components or answer specific research questions. Analytical Linguists work in many different areas and arrive with a wide variety of skills—your specialization might involve natural language processing and understanding, phonology, phonetics, syntax, semantics, ontology, program management, human subject research, experimental design, statistics, corpus linguistics, large scale data acquisition, or any combination. This team is part of some of the most groundbreaking and exciting work at Google. It's our goal to use insights from linguistics and related fields to constantly improve our products.
Natural language technologies (NLU) have the potential to dramatically improve how Google interacts with and serves its users. Conversational assistants have especially great potential for helping people get things done in their everyday lives — but they depend on Google’s ability to understand what people need and how they express those needs.
The NLU team is responsible for developing resources and algorithms to support qualitatively more natural conversational interactions. We combine theoretical insights from linguistics, as well as practical systems experience, to build scalable knowledge-based and data-driven approaches to semantic structure, interpretation and inference.
As an Analytical Linguist, you'll have a broad knowledge of, and experience in, relevant areas of theoretical and computational linguistics, with special emphasis on semantic representations and how they connect with both world knowledge and surface syntax. Expertise with lexical resources, grammar formalisms and computational models that exploit both structured linguistic knowledge and statistical learning techniques is essential. You'll also possess relevant skills with a wide range of linguistic data, along with multilingual expertise.
There is always more information out there, and the Research and Machine Intelligence team has a never-ending quest to find it and make it accessible. We're constantly refining our signature search engine to provide better results, and developing offerings like Google Instant, Google Voice Search and Google Image Search to make it faster and more engaging. We're providing users around the world with great search results every day, but at Google, great just isn't good enough. We're just getting started.
Responsibilities
- Design resources and processes to support a variety of projects related to natural language understanding.
- Extend or design conceptual/theoretical frameworks to support richer models of language use (e.g. incorporating frame semantics, the syntax/semantics interface, discourse and pragmatics, etc).
- Develop methods for analyzing natural language data and using it to improve practical NLP systems.
- Identify needs for linguistic data/corpora/tools.
- Design tasks and frameworks for collecting and evaluating annotated data. Collaborate cross-functionally with linguists, researchers, engineers, and other cross-functional partners in a hybrid research-product environment.
Qualifications
- Masters in a relevant analytical field (e.g. Linguistics, Cognitive Science, Statistics, Mathematics, etc) or equivalent practical experience.
- Experience with theoretical, applied and computational linguistics. Experience with a variety of linguistic and/or ontological representations (e.g. grammar, syntax, semantics, discourse, pragmatics, inference, etc).
- Experience working with large quantities of natural language data. Experience with lexical resources, corpora, NLP algorithms and tools.
Preferred qualifications:
- Ph.D. in a relevant analytical field (e.g. linguistics, cognitive science, statistics, mathematics, etc).
- Experience in designing and implementing data-based research experiments. Experience in managing, directing and/or evaluating the work of others.
- Demonstrated analytical skills and excellent written and verbal communication skills in a technical context.
- Proficiency in coding with Python and/or C++ (for scripting, corpus analysis, etc).
- Demonstrated analytical and design abilities. Ability to prototype ideas, manipulate data and to conduct experiments. Effective communication and collaboration skills.
- Ability to speak and write in one or more non-English languages.


