Principal Applied Scientist, Trusted Supply, Amazon Ads

Amazon is hiring a Principal Applied Scientist, Trusted Supply, Amazon Ads in London, United Kingdom. Level rates it ; you can apply on Amazon.

Level's read on this job

Builds AI ●●●● is how Level rates this applied scientist role: the job is building AI systems.
The posting mentions computer vision, NLP and machine learning among its tools and skills.
Level classes it as an executive role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Principal Applied Scientist leading AI and algorithm development for brand safety, fraud prevention, and trust in Amazon Ads.

computer-visionnlpmachine-learningalgorithmsbrand-safetycomputational-advertisingdistributed-systems

Amazon Advertising is a fast-growing multi-billion dollar business that spans desktop, mobile, and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third-party publishers; and extends across US, EU, and an expanding number of international geographies.

The Trusted Supply organization has the charter to safeguard advertiser trust and ensure high-quality ad impressions across all Amazon Advertising surfaces. We develop advanced algorithms and infrastructure systems to protect advertisers from unsafe content adjacency, low-quality inventory, fraud and privacy threats. Our scope spans a wide variety of problems in computational advertising including brand safety classification, content suitability scoring, risk hunting and proactive threat detection, viewability prediction, Made-for-Advertising (MFA) detection, malvertising identification, and privacy-preserving measurement and integration.

We are looking for an exceptional Principal Applied Scientist to define and drive the science vision across Brand Safety, Suitability, and Risk Hunting as primary areas of focus, while contributing to broader Supply Quality challenges around viewability, privacy-preserving solutions, and data leakage prevention. This is a high-visibility leadership role where your models and systems will process billions of ad impressions daily, directly impacting advertiser confidence, customer experience, and a multi-billion dollar business.

Key job responsibilities
Set the science vision — defining multi-year research directions, establishing the publication roadmap, and driving innovations

Operate across programs — influence modeling frameworks across brand safety, MFA detection, traffic quality, viewability, and 3P integrations; break down silos between science and engineering teams

Act as a thought leader — anticipate industry shifts (privacy regulations, adversarial evolution, GenAI-powered threats), propose counter-strategies before they become critical, and represent Amazon in industry forums (TAG, MRC, IAB)

Hire, mentor, and grow a high-performing team of applied scientists and research engineers; establish a culture of scientific rigor, peer-reviewed publications, and rapid experimentation

Partner with engineering leaders to build efficient, scalable, low-latency production systems that serve models at billions-of-requests-per-day scale

Influence product and business strategy — translate science capabilities into advertiser-facing products (targeting controls, transparency reports, quality guarantees) and quantify business impact

Basic qualifications

Ph.D. in Computer Science, Machine Learning, Statistics, or a highly quantitative field

Experience applying machine learning to real-world problems at scale, with multiple years in a science leadership capacity

Proven track record of leading, mentoring, and growing teams of scientists (5+ scientists)

Deep expertise in NLP, Computer Vision, or multi-modal learning with demonstrated impact in production systems

Strong publication record in top-tier ML/AI conferences (NeurIPS, ICML, KDD, WWW, ACL, EMNLP, CVPR, or equivalent)

Experience with large-scale distributed ML systems processing terabytes of data

Expert-level proficiency in Python and at least one systems language (Java, C++, Scala)

Demonstrated ability to translate ambiguous business problems into well-defined science initiatives with measurable outcomes

Preferred qualifications

Experience with GenAI/LLM-based classification systems at production scale

Experience in computational advertising, ad tech, content moderation, trust & safety, or fraud/abuse detection

Expertise in adversarial machine learning, anomaly detection, or security-oriented ML applications

Familiarity with industry standards: MRC accreditation, TAG certification, brand safety frameworks, IAB content taxonomy

Experience with privacy-preserving ML techniques (federated learning, differential privacy, on-device inference)

Track record of defining org-level research practices and shipping 0-to-1 science products

Experience with real-time inference systems operating at low latency (<10ms) and massive scale (billions of daily predictions)

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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

Principal Applied Scientist, Trusted Supply, Amazon Ads 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

Principal Applied Scientist, Trusted Supply, Amazon Ads is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in GB, London.
The job was posted on August 24, 2026.
Seniority: executive.
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

Computer visionNLPMachine learningAlgorithmsBrand SafetyComputational AdvertisingDistributed Systems

Also common in applied scientist jobs at this level

Deep learning Reinforcement learning TensorFlow Spark AWS scikit-learn

Pay

Typical applied scientist job: $169k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

Questions you could be asked

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?
Tell me about a project where algorithms was part of your work. What did you do?
Tell me about a project where brand safety was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Computer vision, NLP, Machine learning, Algorithms, and Brand 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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