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We are building the next generation of AI-powered advertising intelligence at Amazon Ads. Our team is developing an agent platform that gives every advertiser — regardless of size or sophistication — access to the caliber of strategic campaign management previously available only through dedicated agencies. You will work on agent orchestration, reinforcement learning from expert feedback, trust and permission systems, and scalable inference infrastructure that powers intelligent advertising decisions across Amazon's ad products. This is a greenfield opportunity to define how AI agents operate in one of the world's largest advertising ecosystems. Key job responsibilities - Develop orchestration systems that coordinate multiple specialized AI sub-agents to execute complex, multi-step advertising workflows - Collaborate with cross-functional teams (product, science, partner engineering) to define integration contracts and onboard new consumers to the platform - Build identity, authorization, and permission frameworks that enforce scoped access for diverse caller types - Implement feedback signal pipelines that capture expert judgment and route it into model improvement loops - Design and operate end-to-end evaluation systems that measure recommendation quality, detect regressions, and enforce quality gates - Partner with Applied Scientists to integrate reinforcement learning from human expert feedback into production agent systems - Own system reliability, observability, and performance for always-on agent infrastructure serving thousands of concurrent advertiser accounts A day in the life You might start the morning reviewing eval results from a newly onboarded agent skill, diagnosing why routing accuracy dropped 2%. By mid-morning, you're pairing with an Applied Scientist to tune the feedback pipeline that captures expert signals. After lunch, you're designing the permission model for a new integration. You end the day shipping an observability improvement that surfaces latency bottlenecks across the orchestration chain. Every day is different, but the thread is the same: making AI agents smarter, safer, and more reliable at scale. About the team We're a small, high-ownership team within Amazon Ads building the platform that makes AI agents accessible across the advertising ecosystem. Our charter is greenfield: we're defining how agent intelligence gets exposed to other engineering teams. We work closely with Applied Scientists on learning systems, product teams on advertiser experience, and platform teams on shared infrastructure. The team values builders who ship fast, think in systems, and are comfortable operating in ambiguity. If you want to shape how AI transforms advertising from the ground floor, this is the team.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.