Lead Applied Scientist, Real-Time Personalization

Socket.dev; Sunnyvale (CA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

RSUs

Health insurance

Paid time off

Job summary

Prime Video is seeking an Applied Scientist to own the end-to-end ML lifecycle for content recommendation, from problem framing to production deployment. You will design systems that scale to hundreds of millions of customers and collaborate with cross-functional teams to deliver personalized experiences.

The role emphasizes rigorous experimentation, real-time personalization, and mentoring junior scientists.

Qualifications

• PhD or Master’s degree with 4+ years in CS/CE/ML or related field.
• 3+ years of building models for business applications.
• Experience programming in Java, C++, Python.
• Experience in algorithms, data structures, parsing, numerical optimization, data mining, parallel and distributed computing.

Responsibilities

• Design, develop, and deploy ML models for content recommendation.
• Own the complete ML lifecycle: problem formulation, data analysis, model development, evaluation, and production deployment.
• Build and continuously optimize recommendation systems with real-time latency requirements at scale.
• Design and execute rigorous A/B and multivariate experiments to measure recommendation quality and impact.
• Partner with software engineering to productionize ML models and define serving infrastructure and monitoring.
• Collaborate with product managers and stakeholders to translate business problems into tractable scientific solutions.
• Mentor scientists and engineers on rigorous experimental discipline and ML engineering best practices.

Skills

Java

C++

Python

Education

PhD, or Master’s degree with 4+ years in CS/CE/ML or related field

Job description

Prime Video is seeking an Applied Scientist to own the end-to-end ML lifecycle for content recommendation, from problem framing to production deployment. You will design systems that scale to hundreds of millions of customers and collaborate with cross-functional teams to deliver personalized experiences.

The role emphasizes rigorous experimentation, real-time personalization, and mentoring junior scientists.