AI Language Engineer, Alexa for Shopping

Job summary

DESCRIPTION The Conversational Shopping team is looking for a Language Engineer to drive efficiencies and innovation in its efforts to deliver a seamless, fluent, and engaging experience for AI-assisted shopping. This is an opportunity to join the high-performing team behind Amazon’s Generative AI shopping initiatives such as Rufus AI, Amazon’s Conversational Shopping assistant.

Benefits

Qualifications

• The Conversational Shopping team is looking for a Language Engineer to drive efficiencies and innovation in its efforts to deliver a seamless, fluent, and engaging experience for AI-assisted shopping.
• They will design processes to facilitate the production of high quality editorial data which will allow us to evaluate and improve the Shopping AI experience in different languages.
• behind data annotation tasks and tooling, writing intuitive annotation guidelines and guiding the creation of the
• and achieve the desired Amazon customer outcomes.
• This role requires strong analytical and technical
• Master's degree or above in Computational Linguistics
• Experience owning and executing language data collection projects, including guidelines, labelset and annotation workflow development
• PREFERRED
• 2+ years of computational linguistics, language data processing, semantics, philosophy of language experience
• Experience in development in the last 3 years, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• Our inclusive culture empowers Amazonians to deliver the best results for our customers.

Responsibilities

• Produce, process and manipulate different types of language data, analyze, and provide efficient solutions
• Automate operations and perform data analysis using coding/scripting language (e.g.
• Python)
• Develop LLM-assisted workflows and annotations solutions (e.g.
• LLM-as-a-judge) to support Human-in-the-loop evaluations
• Design and lead editorial data production/collection by defining scope with internal customer teams
• Define clear editorial workflows (SOPs) to meet or exceed the quality bar
• Adopt and design control mechanisms, metrics and methodologies for editorial and annotation quality
• Maximize productivity, process efficiency and quality through streamlined workflows, process standardization, documentation, audits and investigations on a periodic basis.
• Collaborate with editors, applied scientists, engineers, and product managers to deliver the optimal customer experience and define metrics, guidelines, and workflows to continue doing so
• Establish processes and mechanisms to onboard and train editors on an ongoing basis.
• Handle work prioritization and deliver based on business priorities.

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