Build next-generation search navigation AI systems for Amazon's worldwide e-commerce platform, focusing on improving product discovery through personalized refinements and large-scale data pipelines.
Responsibilities
Interface with Applied Scientists, Product Managers, and Program Managers to determine requirements for production systems
Analyze log data to determine future system design and configure parameters
Build scalable and production-ready data pipelines to extract features from petabytes of raw data
Integrate models and algorithms in complex, real-time production systems on immense scale
Design and execute experiments to determine the impact of models and algorithms
Run analysis reports of web-experiments to identify benefits and risk of launching models and algorithms
Serve as liaison to customer/seller-facing partner teams for escalations
Amazon is a multinational technology company and retailer focused on e-commerce, cloud computing, digital streaming, and artificial intelligence. It operates through various segments including Amazon Web Services (AWS), online/physical stores, and device manufacturing.
3+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications using C#, C++, Java, or Perl
3+ years of Object Oriented Design experience
Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field
Experience programming with at least one software programming language
Nice to Have
5+ years of full software development life cycle experience including coding standards, code reviews, source control management, build processes, testing, and operations
Bachelor's degree in computer science or equivalent
Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
Experience in machine learning, data mining, information retrieval, statistics or natural language processing
Experience with Machine Learning and Large Language Model fundamentals including architecture, training/inference lifecycles, and optimization of model execution