Senior Applied Scientist: Generative AI for Next-Gen Ads

Amazon; Seattle (WA); On-site; USD 167,000 - 226,000; Full time

Benefits offered by this job

Health benefits

Job summary

Amazon Ads is seeking a senior ML researcher to build next-generation ML solutions, including generative AI and LLM applications, that transform advertising at scale.

You will own end-to-end projects from ambiguous problems to production-grade models and design experiments with measurable business impact, collaborating with engineers, PMs, and fellow scientists to ship fast and iterate faster.

Qualifications

• 5+ years building ML models for real business applications.
• PhD, or Master's degree + 6 years of applied research experience.
• Strong programming skills in Python, Java, C++, or similar.
• Hands-on experience with deep learning and neural network methods.

Responsibilities

• Research and build next-generation ML solutions — including generative AI and LLM applications — that transform how advertising works
• Own end-to-end projects: from messy, ambiguous problems to deployed, production-grade models
• Design experiments that validate your hypotheses and measure real business impact
• Build models that balance what's best for customers and advertisers — because great advertising should feel helpful, not intrusive
• Work cross-functionally with engineers, PMs, and fellow scientists to ship fast and iterate faster
• Develop scalable ML pipelines that optimize monetization without sacrificing customer experience

Skills

5+ years ML models

Python/Java/C++

Deep learning

LLMs / generative AI

Experimentation

Education

PhD

Master's +6 years applied research

Tools

scikit-learn

TensorFlow

PyTorch

Spark MLLib

MxNet

numpy

scipy

Hadoop

Spark

Job description

Amazon Ads is seeking a senior ML researcher to build next-generation ML solutions, including generative AI and LLM applications, that transform advertising at scale.

You will own end-to-end projects from ambiguous problems to production-grade models and design experiments with measurable business impact, collaborating with engineers, PMs, and fellow scientists to ship fast and iterate faster.