Applied Science Manager, Advertising Trust

Amazon is hiring an Applied Science Manager, Advertising Trust in Bengaluru, India. Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
The posting mentions Python, PyTorch and prompt engineering among its tools and skills.
Level classes it as a lead role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Lead a team of applied scientists building next-generation content moderation intelligence using multimodal machine learning and generative AI.

PythonPyTorch

prompt-engineeringfine-tuningcomputer-visionnlpmachine-learninggenerative-aimultimodal-mlteam-management

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
- 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.

How we rate this

Applied Science Manager, Advertising Trust at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Science Manager, Advertising Trust is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in IN, KA, Bengaluru.
The job was posted on August 11, 2026.
Seniority: lead.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringFine-tuningComputer visionNLPMachine learningGenerative AIMultimodal MLTeam Management

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?
Tell me about a project where machine learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Prompt engineering, Fine-tuning, Computer vision, NLP, and Machine learning. 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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