Alexa+ is the world’s best Generative AI powered personal assistant / agent for consumers. We are seeking an Applied Scientist to join our newly expanding team in India focused on Alexa Conversational Ads and Personalization. In this role, you will build machine learning models that seamlessly and naturally integrate relevant advertising into the Alexa experience while deeply personalizing user interactions. You will work closely with other scientists, engineers, and product managers to take models from conception to production.
Key job responsibilities
Design, develop, and evaluate innovative machine learning and deep learning models for natural language processing (NLP), recommendation systems, and personalization.
Conduct hands-on data analysis and build scalable ML pipelines.
Design and run A/B experiments to measure the impact of new models on customer experience and ad performance.
Collaborate with software development engineers to deploy models into high-scale, real-time production environments.
Basic Qualifications
- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- 1+ years of building models for business application experience
- PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in machine learning, data mining, information retrieval, statistics or natural language processing
Preferred Qualifications
- Have publications at top-tier peer-reviewed conferences or journals
- Experience with and deep understanding of advertising technologies (i.e., ad server, DSP)
- Experience (technical and operational) with multiple domain areas of programmatic advertising technologies (DSP, RTB, bid shading, machine learning optimization, ad verification, ad tracking, ad attribution, etc.)
- Experience building large-scale machine learning models and infrastructure for online recommendation, ads ranking, personalization, or search
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