About the job
Alexa International Science team is looking for a passionate, talented, and inventive Senior Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. At this level, you will drive cross-team scientific strategy, influence partner teams, and deliver solutions that have broad impact across Alexa's international products and services.
Responsibilities
develop novel algorithms and modeling techniques to advance the state of the art with LLMs, particularly delivering industry-leading scientific research and applied AI for multi-lingual applications; leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains; thrive in fast-paced environment, like to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and are able to influence and align multiple teams around a shared scientific vision
Qualifications
Minimum
PhD, or Master's degree
4+ years of applied research experience
Experience working across teams and influencing teams that are not your own
Experience with any programming language such as Python, Java, C++
6+ years of building machine learning models or developing algorithms for business application experience
Knowledge of standard speech and machine learning techniques
PhD in CS, CE, ML, or related field with 4+ years of relevant post-PhD research experience; or Master's degree with 7+ years of equivalent experience
Deep expertise in state-of-the-art LLM architectures, training, evaluation, and post-training techniques (SFT, DPO, RLHF, RLAIF)
Proven track record of delivering scientifically complex solutions into production
Preferred
solid understanding of machine learning, speech and/or natural language processing, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field