Language Engineer, Artificial General Intelligence - Data Services

• Masters’s or higher degree in a relevant field (computational linguistics or equivalent field with computational analysis)
• 2+ years experience in computational linguistics or language data processing
• Experience with language annotation and other forms of data markup
• Experience with scripting languages, such as Python
• Experience working with speech and text language data in multiple languages
• Excellent communication, strong organizational skills and very detailed oriented
• Comfortable working in a fast paced, highly collaborative, dynamic work environment
PhD in Computational Linguistics (or equivalent field with computational emphasis)
• Expertise in bootstrapping language data collections in a quickly changing environment
• Comfortable working with speech and text language data in multiple languages
• Experience in writing grammars and building FSTs
• Experience with statistical language modeling
• Practical knowledge of version control and agile development
• Familiarity with database queries and data analysis processes (SQL, R, Matlab, etc.)
• Willingness to support several projects at one time, and to accept reprioritization as necessary
• Able to think creatively and possess strong analytical and problem solving skills

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The Amazon Artificial General Intelligence (AGI) Data Services organization is looking for a Language Engineer with experience in dataset construction, linguistic annotation, dialog/semantic schemas, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, engineers, and data scientists to ensure that our AI systems are aligned with human policies and preferences.

Key job responsibilities
Specifically, the Language Engineer will:
• Design data collection/creation tasks in response to science needs: author instructions, define and implement quality targets and mechanisms, provide day-to-day coordination of data collection efforts (including planning, scheduling, and reporting), and be responsible for the final deliverables
• Analyze and extract language-related insights from large amounts of data
• Build tools or tool prototypes for data analysis or data authoring, using Python or another scripting language
• Use modeling tools to bootstrap or test new functionalities
• Collaborate with scientists and software engineers to evaluate performance of language models
• Handle competing requests from a range of data customers