About the job
Senior Software Development Engineer to architect and own large-scale training data systems and experimentation frameworks that power Amazon’s next-generation shopping AI. This is a hybrid role that combines distributed systems engineering with strong data science rigor to transform customer interactions — including search refinement, clicks, add-to-cart, and purchase behavior — into measurable learning signals that improve the shopping experience for Amazon customers.
Responsibilities
Architect scalable training data systems that enable AI models to continuously learn from live customer behavior
Build high-throughput pipelines that transform production engagement signals into structured training datasets
Analyze model behavior to generate insights into model quality and identify gaps in training data coverage
Design and refine training data recipes, including sampling strategies, signal weighting, filtering, and dataset composition
Apply statistical rigor and experimentation to validate training signal quality and model improvements
Ensure strong data security, governance, and compliance standards across production data workflows
Provide technical leadership across distributed systems and ML training infrastructure
Qualifications
Minimum
5+ years of non-internship professional software development experience
5+ years of programming with at least one software programming language experience
5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience as a mentor, tech lead or leading an engineering team
Experience with vLLM, SGLang, TensorRT or similar platforms in production environments
Experience with CUDA kernels or ML/low-level kernels
Preferred
5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent
Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution