AmazonPosted 3mo ago

Applied Scientist, Trust CX Innovations&AI Policy

Applied Scientist, Trust CX Innovations&AI Policy at Amazon scores 92 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengalurumidfull-time

AI in this role

prompt-engineeringfine-tuningai-safety

The Trust CX Innovations team is looking for an Applied Scientist with strong background in Generative AI space to build solutions that help in upholding customer trust for Alexa+.

As an Applied Scientist in Trust CX innovations, you will be at the forefront of developing innovative solutions to critical challenges in AI trust and privacy. You'll lead research in trust-preserving machine learning techniques. We are working on revolutionizing the way Amazonians work and collaborate. You will help us achieve new heights of productivity through the power of advanced generative AI technologies.

Key job responsibilities
- Lead research initiatives in generative AI, focusing on LLMs, multimodal models, and frontier AI capabilities
- Develop innovative approaches for model optimization, including prompt engineering, few-shot learning, and efficient fine-tuning
- Pioneer new methods for AI safety, alignment, and responsible AI development
- Design and execute sophisticated experiments to evaluate model performance and behavior
- Lead the development of production-ready AI solutions that scale efficiently
- Collaborate with product teams to translate research innovations into practical applications
- Guide engineering teams in implementing AI models and systems at scale
- Author technical papers for top-tier conferences
- File patents for novel AI technologies and applications

A day in the life
You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our trust-preserving experiences. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership.

About the team
Who We Are:
Trust CX Innovations is a strategic innovation team within Amazon Devices & Services that focuses on advancing AI technology while prioritizing customer trust and experience. Our team operates at the intersection of artificial intelligence, privacy engineering and customer-centric design.

Our Mission:
To pioneer trustworthy AI innovations that delight customers while setting new standards for privacy and responsible technology development. We aim to transform how Amazon builds AI products by creating solutions that balance innovation with customer trust.

Basic qualifications

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language

Preferred qualifications

- Experience in professional software development
- Background in AI safety and alignment research

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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringFine TuningAI Safety

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you think about the risk of an AI system in this kind of role failing silently?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Prompt Engineering, Fine Tuning, and AI Safety. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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