Applied Scientist

About the Role

The Alexa Sensitive Content Intelligence (ASCI) team is developing Responsible AI (RAI) solutions for Alexa+, ensuring it provides useful information responsibly. This role involves pioneering breakthrough solutions in Responsible AI at Amazon's scale, shaping how AI learns, evolves, and earns customer trust. The work directly protects customer trust and defines industry standards for responsible AI.

Key Responsibilities

• Build AI safety systems that protect millions of Alexa customers.
• Develop and refine algorithms to detect and eliminate false information.
• Build models to identify vulnerabilities exploited by bad actors.
• Train systems that understand human values across diverse cultures.
• Apply state-of-the-art NLP and computer vision techniques to content classification and safety challenges.
• Transform cutting-edge research into production systems.
• Contribute to identifying, defining, and mitigating AI safety risks early.
• Build deep expertise in responsible AI.
• Work within multi-agent architectures, implementing components for distributed AI systems with human oversight and control.
• Drive innovations from consensus-driven reasoning frameworks to sophisticated orchestration patterns.
• Experiment with breakthrough techniques that push the boundaries of AI safety.
• Partner with engineering to transform research into production-ready solutions.
• Tackle complex optimization challenges.
• Brainstorm with Product teams to translate visions into concrete objectives.
• Review impactful work and guide deployment decisions.
• Mentor the next generation of AI safety leaders.

Requirements & Tech Stack

• Deep expertise in state-of-the-art NLP and Large Language Models.
• Track record of building scalable ML systems.
• Passion for impactful research where frontier science meets real-world responsibility at scale.
• Excitement about solving problems that will shape the future of AI.

Qualifications

• Experience building machine learning models or developing algorithms for business application.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.
• Experience programming in Java, C++, Python, or related language.
• Experience in state-of-the-art deep learning models architecture design, deep learning training and optimization, and model pruning.
• Experience using Unix/Linux.
• Experience with programming languages such as Python, Java, C++.