Applied Scientist III- Recruiting AI Agents, Recruiting Agents & Candidate Voice

Key job responsibilities

• Design and architect AI-powered agentic solutions that help candidates navigate Amazon's hiring process, including scoping requirements, identifying dependencies and constraints, and creating robust scientific and technical designs that balance candidate experience with system scalability.
• Implement and deploy conversational AI agents leveraging state-of-the-art LLM and GenAI technologies to enable candidates to explore job opportunities, understand role requirements, and receive personalized guidance throughout their hiring journey.
• Develop rigorous evaluation frameworks to measure agent effectiveness, candidate satisfaction, and hiring outcomes—continuously iterating on models to improve accuracy, fairness, and user experience across millions of candidate interactions.
• Collaborate cross-functionally with Research Scientists, Software Engineers, and Product teams to integrate agentic solutions into Amazon's candidate-facing platforms, ensuring seamless deployment and alignment with broader Talent Acquisition goals.
• Drive innovation in agentic AI research by staying current with advances in NLP, LLMs, and autonomous agent architectures, while contributing to the scientific community through publications, internal tech talks, and knowledge sharing.

About the team

Our team focuses on understanding and improving the experience of both job seekers and the recruiters who support them. You'll be at the intersection of people, data, and technology—solving fascinating problems that directly impact how we hire the best talent globally.

Basic Qualifications

• 5+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.