Amazon Sr Language Engineer AWS Q Business 

United States, California 

737218926

As a Senior Language Engineer in AWS Q for Business Data Team, you will start by diving deep into a high profile project to launch a new product offering. You will consult with stakeholders in science, engineering, and product teams to understand the role data plays in developing models that meet customer needs. You will lead the data collection and annotation strategy, with a focus on iterative analysis and course correction. You will use your hands-on data analytics skills and up-to-date knowledge of machine learning (ML) techniques when collaborating with scientists to ensure that datasets are optimized for model performance. You will raise issues regarding data availability and level of effort for data collection and propose solutions to overcome potential obstacles.You will then expand your scope to plan and implement high impact initiatives. You will gain an understanding of the language engineering needs across the various programs supported by the Data Team and coordinate with your colleagues to identify opportunities for innovation. You will use data-driven reasoning to quantify the benefits to AI/ML programs and to secure the necessary resources.You will also function as a technical expert in data-centric AI, staying up to date in developments in the field and sharing your knowledge with colleagues across AWS. You will experiment with new techniques in data collection and annotation. You will collaborate with science teams to develop more effective workflows and influence the roadmap for tooling to support data collection processes.

* PhD in Computational Linguistics (or similar quantitative field), or equivalent experience * 3+ years industry experience developing natural language processing products * Proficiency in scripting and analytics tools such as Python or R * Experience leading data collection projects, including annotation workflow and data quality assessments * Experience in developing and evaluating data annotation and data quality metrics * Understanding of the ML model development process * Strong written and verbal communication skills, with an ability to present complex technical information in a clear and concise manner to a variety of audiences

* Experience with programmatic approaches to annotation, including weak supervision and active learning * Experience with synthetic dataset creation * Experience working with a diverse array of languages or language varieties