About the job
As an Applied Scientist on LPEX, you will be a technical owner and science leader across the following areas:
* Design, develop, and deploy machine learning models for content recommendation, viewer engagement optimization, and real-time personalization at the scale of hundreds of millions of Prime Video customers.
* Own the complete ML lifecycle: problem formulation, data analysis, feature engineering, model development, offline and online evaluation, and reliable production deployment.
* Build and continuously optimize recommendation systems with strict real-time latency requirements, ensuring that personalization decisions are delivered at speed and scale.
* Design and execute rigorous A/B and multivariate experiments to measure recommendation quality, understand causal drivers of engagement, and iterate rapidly toward customer impact.
* Partner with software engineering teams to productionize ML models, defining requirements for serving infrastructure, data pipelines, and model monitoring and observability.
* Collaborate with product managers and cross-functional stakeholders to translate ambiguous business problems into well-scoped, tractable science solutions.
* Mentor scientists and engineers on the team, setting a high bar for scientific rigor, experimental discipline, and ML engineering best practices.
Responsibilities
Design, develop, and deploy machine learning models for content recommendation, viewer engagement optimization, and real-time personalization at the scale of hundreds of millions of Prime Video customers.
Own the complete ML lifecycle: problem formulation, data analysis, feature engineering, model development, offline and online evaluation, and reliable production deployment.
Build and continuously optimize recommendation systems with strict real-time latency requirements, ensuring that personalization decisions are delivered at speed and scale.
Design and execute rigorous A/B and multivariate experiments to measure recommendation quality, understand causal drivers of engagement, and iterate rapidly toward customer impact.
Partner with software engineering teams to productionize ML models, defining requirements for serving infrastructure, data pipelines, and model monitoring and observability.
Collaborate with product managers and cross-functional stakeholders to translate ambiguous business problems into well-scoped, tractable science solutions.
Mentor scientists and engineers on the team, setting a high bar for scientific rigor, experimental discipline, and ML engineering best practices.
Qualifications
Minimum
PhD, or Master's 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 in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
Experience building complex recommendations or personalization systems that have been successfully delivered to customers at significant scale.