Applied Scientist II, Alexa International Team

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services.


Key job responsibilities
As an Applied Scientist with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams.

A day in the life
* Analyze, understand, and model customer behavior and the customer experience based on large-scale data.
* Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants.
* Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF.
* Set up experimentation frameworks for agile model analysis and A/B testing.
* Collaborate with partner teams on LLM evaluation frameworks and post-training methodologies.
* Contribute to end-to-end delivery of solutions from research to production, including reusable science components.
* Communicate solutions clearly to partners and stakeholders.
* Contribute to the scientific community through publications and community engagement.