Applied Scientist II, Prime Video Personalization and Discovery Science

Job Summary
:

Prime Video is a leading entertainment platform that offers a wide range of programming for its customers. The Applied Scientist II will focus on developing advanced models for content understanding and customer behavior analysis, collaborating with engineers and product managers to enhance the Prime Video user experience.

Responsibilities
:

• Develop foundation models for content understanding using state-of-the-art deep learning and multimodal learning techniques to analyze video and text

• Build time sequence foundation models to understand and predict customer behavior patterns and viewing trajectories

• Work closely with engineers and product managers to design, implement and launch solutions end-to-end across various Prime Video experiences

• Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses

• Effectively communicate technical and non-technical ideas with teammates and stakeholders

• Stay up-to-date with advancements and the latest modeling techniques in foundation models, multimodal learning, and time series analysis

• Publish your research findings in top conferences and journals

Qualifications
:
Required
:

• PhD, or Masters degree and 4+ years of CS, CE, ML or related field experience

• 3+ years of building models for business application experience

• Experience programming in Java, C++, Python or related language

• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred
:

• Experience using Unix/Linux

• Experience in professional software development

Company
:

Prime Video delivers an entertainment destination unlike any other—fast, reliable, personal, effortless, and delightful. Founded in 2006, the company is headquartered in Seattle, USA, with a team of 10001+ employees. The company is currently Late Stage.