Overview Senior Applied Scientist roles at Amazon Ads focus on building intelligent, autonomous ML systems that help Cross Border Sellers scale across multiple marketplaces. You work on medium-to-large, ambiguous problems, invent new methods, and validate them with experiments to deliver measurable business impact. The role blends science depth with product focus and hands-on engineering, mentoring others while tackling the hardest technical challenges. You will shape campaign automation powered by GenAI and advance advertiser growth within a responsible AI framework.
Compensation / Benefits- health insurance (medical, dental, vision, prescription)
- 401(k) matching
- paid time off
- par ental leave
- restricted stock units (RSUs)
- sign-on payments
Responsibilities- Understand cross-border advertiser pain points and build science-driven solutions to accelerate growth
- Develop agentic ML systems that autonomously create, structure, and manage ad campaigns across marketplaces
- Innovate on GenAI-powered advertising and advance autonomous programs like auto-targeting and global lift-and-shift
- Develop models along the campaign lifecycle (discovery, ranking, readiness, demand, bid/budget optimization) using appropriate ML approaches
- Define datasets and signals for training and evaluating systems (advertiser data, cross-marketplace signals, impressions, clicks, conversions, search-term performance)
- Own components of the agentic architecture (planning, tool use, reasoning frameworks) and establish evaluation and safety methodologies
- Contribute hands-on by writing production-grade code and deploying pipelines (Spark/EMR, Airflow) and online serving
- Mentor scientists and engineers, reviewing designs and communicating results to leaders
Key requirements- 5+ years of building ML models for business applications
- PhD, or Master's degree with 6+ years of applied research experience
- Programming experience in Java, C++, Python or related languages
- Experience with neural deep learning methods and machine learning
- Mentorship and team leadership
- Clear, cross-functional communication with product and engineering
- Problem-solving and experimental design
- Neural deep learning methods and ML
- GenAI, LLM/reasoning methods
- Production-grade software development (Python/Java/C++)
