Amazon Applied Scientist II Customer Engagement Technology 

United States, California, East Palo Alto 

697871636

Key focus areas include:
2. Lifelong Learning: Researching continuous learning approaches for injecting new domain knowledge while retaining the model's foundational abilities and prevent catastrophic forgetting.
3. Agentic Systems: Developing a modular agentic framework to handle multi domain conversations through appropriate system abstractions.
4. Complex Multi-turn Instruction Following: Identifying approaches to guarantee compliance with instructions that specify standard operating procedures for handling multi-turn complex scenarios.
5. Inference-Time Adaptability: Researching inference-time scaling methods and improving in-context learning abilities of custom models to enable real-time adaptability to new features, actions, or bug fixes without solely relying on retraining.
6. Context Adherence: Exploring methods to ground responses in specific customer attributes, account information, and behavioral data to prevent hallucinations and ensure high-fidelity responses.
7. Policy Grounding: Investigating techniques to align bot behavior with evolving company policies by grounding on complex, unstructured policy documents, ensuring consistent and compliant actions.
1. End to End Dialog Policy Optimization: Researching alignment approaches to optimize successful dialog completions.
2. Scalable Evaluations: Developing automated approaches to evaluate quality of experience, and correctness of agentic resolutionsKey job responsibilities
1. Research and development of LLM-based chatbots and conversational AI systems for customer service applications.
2. Design and implement state-of-the-art NLP and ML models for tasks such as language understanding, dialogue management, and response generation.4. Develop and implement strategies for data collection, annotation, and model training to ensure high-quality and robust performance of the chatbots.
5. Conduct experiments and evaluations to measure the performance of the developed models and systems, and identify areas for improvement.7. Collaborate with internal and external research communities, participate in conferences and publications, and contribute to the advancement of the field.A day in the life
Benefits Summary:1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan

- Master's degree
- 2+ years of building machine learning models or developing algorithms for business application experience

- PhD
- 3+ years of building machine learning models or developing algorithms for business application experience