AmazonPosted 1mo ago

Applied Science Manager, Advertising Trust

Applied Science Manager, Advertising Trust at Amazon scores 99 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, Bengaluruseniorfull-time

AI in this role

prompt-engineeringfine-tuningcomputer-visionnlp

Amazon Ads delivers advertising experiences across Amazon's owned-and-operated properties and third-party networks, reaching hundreds of millions of customers worldwide. Within Amazon Ads, Advertising Trust is the science-first organization responsible for ensuring every ad shown to customers meets Amazon's content policies — at massive scale, across all ad formats and global marketplaces.
The Ads Trust Science team builds the ML systems that automate content moderation decisions: multimodal classification, retrieval-based labeling, LLM reasoning, and agentic self-improvement architectures. This requires inventing new approaches at the intersection of computer vision, NLP, information retrieval, and generative AI.
We are seeking an Applied Science Manager to lead a team of applied scientists building next-generation content moderation intelligence. You will own the science roadmap for one of the highest-impact automation programs in Amazon Advertising, defining how multimodal content understanding, retrieval-first classification, and LLM-based reasoning combine into a production system that serves global advertising at scale.

Key job responsibilities
* Lead a team of applied scientists working across multimodal ML (vision-language models, video understanding), large-scale retrieval systems (embedding-based similarity and deduplication), and generative AI (LLM-based policy reasoning, knowledge distillation, agentic architectures, reinforcement learning).
* Define the science strategy for ads trust.
* Own end-to-end delivery of ML solutions: problem formulation, offline experimentation, online A/B testing, and production deployment. Your models directly move automation and defect metrics reported to senior leadership.
* Build and grow scientists — hire, mentor, and develop team members. Raise the science bar through structured review processes and a publication culture within Amazon.
* Partner with engineering, product, and operations teams to translate science investments into measurable automation improvements. Influence roadmaps across dependent teams.
* Communicate science strategy and results to senior leadership through narratives, technical deep-dives, and roadmap documents.

Basic qualifications

- 8+ years of applied research experience
- 4+ years of scientists or machine learning engineers management experience
- PhD, or Master's degree and 8+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience in several of the following areas: machine learning, statistics, deep learning, natural language processing, or information retrieval
- 4+ yrs in managing team of 5-15 members

Preferred qualifications

- Experience building production ML systems at Internet scale, especially involving multimodal deep learning, generative AI, or large-scale retrieval
- Track record of delivering automation or classification systems with measurable business impact
- Experience with content moderation, trust & safety, or policy enforcement systems
- Publications in top-tier ML/AI venues (NeurIPS, ICML, CVPR, KDD, ACL, AAAI)
- Experience with LLMs (fine-tuning, distillation, RLHF, prompt engineering)
- Demonstrated ability to define and drive science roadmaps that influence product and business strategy

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.

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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 TuningComputer VisionNlp

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?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Prompt Engineering, Fine Tuning, Computer Vision, and Nlp. 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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