Senior Applied Scientist — AI-Driven Seller Growth

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

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Science in Seattle is seeking a Sr. Applied Scientist to advance Seller growth with rigorous ML, RL, and GenAI solutions.

You will mentor teams, design roadmaps for large-scale projects, and collaborate with engineering to ensure responsive systems and impactful business outcomes. You will drive measurement-driven research, craft innovative prompts for AI tooling, and present findings to both internal teams and the broader scientific community.

Qualifications

• 4+ years of applied research experience.
• 3+ years of building machine learning models for business applications.
• PhD, or Master’s degree with 6+ years of applied research experience.
• Experience programming in Java, C++, Python or related language.
• Experience with neural deep learning methods and machine learning.

Responsibilities

• Identify opportunities to improve SP growth and translate into science problems using ML, RL, GenAI.
• Mentor applied scientists and ensure high technical rigor in MLOps.
• Design and lead roadmaps for complex science projects to add long-term value.
• Collaborate with engineering partners to meet latency and system constraints.
• Identify high-risk directions and simulate new research directions to deliver.
• Communicate science innovations to internal and external scientific communities.

Skills

Applied research

ML modeling

Java

C++

Python

Deep learning

Education

PhD or Master’s with 6+ years

Tools

Hadoop

Spark

MxNet

TensorFlow

scikit-learn

NumPy

SciPy

Job description

Amazon Science in Seattle is seeking a Sr. Applied Scientist to advance Seller growth with rigorous ML, RL, and GenAI solutions.

You will mentor teams, design roadmaps for large-scale projects, and collaborate with engineering to ensure responsive systems and impactful business outcomes. You will drive measurement-driven research, craft innovative prompts for AI tooling, and present findings to both internal teams and the broader scientific community.