Applied Scientist II, Brand Registry
Amazon · Toronto, Canada · Applied Science
About this role
Amazon is hiring a mid-level Applied Scientist in the machine learning function based in Toronto, Canada. The posting calls out experience with Python, Java, LLMs, Prompt Engineering. Compensation is listed at C$149,300–C$249,300 per year.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Toronto, Canada
- Department
- Applied Science
- Posted
- May 4, 2026
More roles at Amazon
Job description
from Amazon careersThe Brand Registry team is seeking an Applied Scientist to tackle complex, high-impact problems that directly affect millions of brands, selling partners, and customers on Amazon. You will design, develop, and deploy AI solutions—leveraging large language models (LLMs) and agentic AI frameworks—to power intelligent automation that augments human decision-making and drives autonomous outcomes at scale. What You'll Do -Build agent-based AI systems that reason, plan, and act like domain experts progressing from decision-support tools to fully autonomous solutions -Own the end-to-end ML lifecycle, from problem formulation and data analysis through experimentation, model development, and production deployment -Work backwards from data insights and customer feedback to identify the highest-value science opportunities and translate them into scalable machine learning solutions -Partner closely with product managers and engineering teams to define requirements, iterate rapidly, and launch solutions that deliver measurable business impact -Collaborate with domain experts across Amazon to pioneer innovative approaches to unsolved problems in brand protection and seller experience What We're Looking For -Technical depth: Extensive hands-on experience in Machine Learning, with a strong focus on Generative AI and LLM-based applications (e.g., fine-tuning, prompt engineering, retrieval-augmented generation, multi-agent orchestration) -End-to-end delivery: Proven track record of driving large-scale ML initiatives from conception…