Amazon is looking for a creative Applied Scientist to tackle some of the most interesting problems on the leading edge of information retrieval (IR) and machine learning (ML) with our Alexa Artificial Intelligence (AI) team. Alexa AI aims to reinvent search and information retrieval for a voice-forward, multi-modal future. We enable customers to interact with unstructured and semi-structured content via a broad range of customer experiences including question answering, summarization, search, and multi-turn dialogues.
Key job responsibilities
If you are looking for an opportunity to develop innovative solutions to deep technical problems having a massive customer impact, this might be the role for you! As an Applied Scientist, you will work with smart, passionate colleagues in a fast-paced environment. You will develop and help deploy novel, scalable algorithms to advance the state-of-the-art in technology areas at the intersection of IR and ML. You will keep up with relevant research in the field of IR and publish your work in top-tier conferences. You will contribute to a multi-year research roadmap, enabling the team to focus on the right technical challenges to delight our customers.
Ph.D. in CS or equivalent experience, 2+ yr. experience developing NLP/QA Models, model deployment, statistical analysis and writing/presentation to non-scientist stakeholders.
- PhD/MS in Computer Science, Machine Learning, NLP, Speech or a related quantitative field
- Experience with at least one deep learning framework
- Experience programming in Python as well as at least one low-level language, e.g. Rust, C, C++
- 2+ years postdoc or industry experience
- Experience in building information retrieval, machine learning, and natural language processing systems
- Excellent written and verbal communication skills
- Publications in top-tier IR, ML, and NLP conferences and journals
- Proficiency in model development, model validation and model implementation for large-scale applications
- Ability to convey mathematical results to non-science stakeholders
- Strength in clarifying and formalizing complex problems