Applied Scientist – Recommender AI & Large-Scale ML

Amazon; Seattle, Northern (WA, KY); Hybrid; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is seeking a scientist to advance state-of-the-art machine learning and recommendation systems in Seattle, WA. You will work with large datasets and massive compute resources to study customer behavior and push the boundaries of models deployed on the retail site.

You will develop AI solutions for recommendations using deep learning, LLMs, reinforcement learning, distillation, and optimization, collaborating with engineers and product managers to design, implement and launch end-to-end

Qualifications

• 3+ years of modeling experience for business applications.
• PhD or Master's plus 4+ years in CS/CE/ML or related field.
• Experience programming in Java, C++, Python.
• Experience in algorithms, data structures, numerical optimization, data mining, parallel and distributed computing.

Responsibilities

• Develop state-of-the-art models to optimize the shopping experience on the retail website.
• Build AI solutions for recommendations using DL, LLMs, RL, distillation, and optimization.
• Collaborate with engineers and product managers to deploy end-to-end.
• Design offline and online experiments (A/B) to evaluate solutions with data analyses.
• Communicate complex ideas clearly to teammates and stakeholders.

Skills

Modeling experience

Programming: Java/C++/Python

ML/Data mining

Education

PhD or Master’s + 4+ years in CS/ML

Tools

Unix/Linux

Professional software development

Job description

Amazon is seeking a scientist to advance state-of-the-art machine learning and recommendation systems in Seattle, WA. You will work with large datasets and massive compute resources to study customer behavior and push the boundaries of models deployed on the retail site.

You will develop AI solutions for recommendations using deep learning, LLMs, reinforcement learning, distillation, and optimization, collaborating with engineers and product managers to design, implement and launch end-to-end