Applied Scientist, Real-Time Personalization at Scale

Amazon; Sunnyvale (CA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance

RSUs

Sign-on bonus

401(k) matching

Paid time off

Job summary

Amazon’s Prime Video team seeks an Applied Scientist for the Linear Personalization Experience (LPEX) group. You will own end-to-end ML lifecycle, from problem framing to production deployment, building systems that personalize Prime Video content for millions of customers.

You’ll work on state-of-the-art recommendation models, operate in a fast-paced environment, and collaborate with cross-functional teams to deliver measurable customer impact.

Qualifications

• PhD or Master’s degree with 4+ years CS/CE/ML or related field experience.
• 3+ years building models for business applications.
• Proficiency in Java, C++, Python or related language.
• Experience with ML models, recommendation systems, or personalization.

Responsibilities

• Design, develop, and deploy ML models for content recommendation at scale.
• Own the complete ML lifecycle from problem formulation to production deployment.
• Build and optimize real-time, large-scale recommendation systems with low latency.
• Design and run A/B and multivariate experiments to measure impact on engagement.
• Collaborate with software engineers to productionize ML models and data pipelines.
• Partner with product managers to translate business problems into scientific solutions.
• Mentor teammates on rigorous ML research and engineering practices.

Skills

Java

C++

Python

Algorithms

Data structures

Numerical optimization

Data mining

Distributed computing

High-performance computing

Education

PhD or Master’s degree

Job description

Amazon’s Prime Video team seeks an Applied Scientist for the Linear Personalization Experience (LPEX) group. You will own end-to-end ML lifecycle, from problem framing to production deployment, building systems that personalize Prime Video content for millions of customers.

You’ll work on state-of-the-art recommendation models, operate in a fast-paced environment, and collaborate with cross-functional teams to deliver measurable customer impact.