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Alexa International is looking for passionate, talented, and inventive Senior Applied Scientists to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. Senior applied scientists will drive cross-team scientific strategy, influence partner teams, and deliver solutions that have broad impact across Alexa's international products and services.
Key job responsibilities
As a Senior 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, particularly delivering industry-leading scientific research and applied AI for multi-lingual applications - a challenging area for the industry globally. Your work will directly impact our global customers in the form of products and services that support Alexa+. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains. The ideal candidate possesses a 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. They 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.
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 advanced and innovative techniques like SFT, DPO, Reinforcement Learning (RLHF and RLAIF) for supporting model performance specific to a customer's location and language.
. Quickly experiment and set up experimentation framework for agile model and data analysis or A/B testing.
. Contribute through industry-first research to drive innovation forward.
. Drive cross-team scientific strategy and influence partner teams on LLM evaluation frameworks, post-training methodologies, and best practices for international speech and language systems.
. Lead end-to-end delivery of scientifically complex solutions from research to production, including reusable science components and services that resolve architecture deficiencies across teams.
. Serve as a scientific thought leader, communicating solutions clearly to partners, stakeholders, and senior leadership.
. Actively mentor junior scientists and contribute to the broader internal and external scientific community through publications and community engagement.
- PhD, or Master's degree and 10+ years of applied research experience
- 5+ years of building machine learning models for business application experience
- Experience with neural deep learning methods and machine learning
- Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Deep expertise in state-of-the-art LLM architectures, training, evaluation, and post-training techniques (SFT, DPO, RLHF, RLAIF)
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience in professional software and systems development
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
Job ID: 144557263
