Senior Applied Scientist, Personalization & Discovery

Amazon Inc.

Sunnyvale (CA)

On-site

USD 172,000 - 222,000

Full time

14 days+

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Benefits offered by this job

RSUs
Health insurance
401(k) matching

Job summary

Amazon Science in Sunnyvale, CA, seeks a machine learning scientist to advance foundation models for content understanding and multimodal learning across Prime Video surfaces. You will own end-to-end research-to-production solutions and collaborate with engineers and product managers.

The role emphasizes hands-on ML, opportunities to publish findings, and leadership in research while helping personalize recommendations to improve viewing experiences for customers.

Qualifications

  • PhD or Master's degree in CS, CE, ML or related field with 4+ years of relevant experience.
  • Strong programming skills in Java, C++, Python.
  • Experience with algorithms, data structures, data mining, and parallel computing.

Responsibilities

  • Develop foundation models for content understanding using deep learning and multimodal learning.
  • Build time-series models to understand customer behavior and viewing trajectories.
  • Collaborate with engineers and product managers to deploy end-to-end solutions.
  • Design offline and online experiments to evaluate proposals using data analyses.
  • Communicate complex ideas clearly to teammates and stakeholders.
  • Stay updated on state-of-the-art modeling techniques and publish findings.

Skills

3+ years experience
Java
C++
Python
Algorithms & data structures
Distributed computing

Education

PhD or Master's in CS/CE/ML or related field

Tools

Unix/Linux

Job description

Amazon Science in Sunnyvale, CA, seeks a machine learning scientist to advance foundation models for content understanding and multimodal learning across Prime Video surfaces. You will own end-to-end research-to-production solutions and collaborate with engineers and product managers.

The role emphasizes hands-on ML, opportunities to publish findings, and leadership in research while helping personalize recommendations to improve viewing experiences for customers.

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