Applied Scientist III, Advertising Trust

• Amazon Advertising Trust is on a mission to redefine how trust is enforced at internet scale, and we are looking for exceptional scientists to help lead the way. We are building systems that reason about advertising content the way a trained human would, at a industry leading volume and speed, and that keep working when the rules change underneath them.
• At Ads Trust Science we leverage machine learning, generative AI, and large-scale retrieval to solve some of the most complex decision problems for ads worldwide. Every ad shown across Amazon's owned-and-operated properties and third-party networks, in every format and every global marketplace, depends on decisions our systems make. We are just getting started.
• As a Senior Applied Scientist you will be at the forefront of this work. You will own science problems end to end, bridging the gap between research and production impact. You will not be handed a specification. You will be given a customer problem, a system that partly solves it, and the latitude to decide what to build next.
• This is a unique opportunity to work at the intersection of multimodal understanding, large language models, and large-scale retrieval, on a problem where the science is genuinely unsettled. You will work alongside scientists whose models run in front of hundreds of millions of customers, at a scale that changes which approaches are even possible.

• A day in the life
• - Ship a model into production, then find and document where it fails once real traffic reaches it.
• - Drive the technical discussion, inside your team and with partner teams, on how to close that gap.
• - Bring a fresh angle to a problem that is not yours and help a peer get further than they would have alone, building trust across the team in the process.
• - Mentor while staying hands-on. At this level the design and the code are both yours.

• About the team
• Ads Trust Science is a group of scientists who would rather change what the team measures than optimise the wrong number. We value curiosity, rigour, and a bias for action. We expect people to disagree with evidence, to say clearly what did not work, and to iterate quickly toward the solutions that matter.

Basic qualifications

• - PhD, or Master's degree and 6+ years of applied research experience
• - Experience programming in Java, C++, Python or related language
• - Experience with neural deep learning methods and machine learning

Preferred qualifications

• - Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
• - PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field

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