Amazon.com Services LLC
Technology
SoftwareDevelopmentManager,StoresFoundationalAI-SFAI
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“Software Development Manager, Stores Foundational AI -SFAI at Amazon.com Services LLC. Skills: AI, ML infrastructure, Data engineering, Distributed systems. Lead engineers to design training data systems. Build scalable training data systems”
What You'll Achieve.
Improve model performance; Ensure measurable model improvements; Accelerate AI model iteration; Impact Amazon shopping experience
Industry & Context.
Analyze model behavior; Identify model quality gaps; Translate insights into improvements
What They're Looking For.
Must Have
5+ years Software Engineer experience, 3+ years engineering team management, 3+ years technical leadership, 3+ years project management, Knowledge of engineering practices, Experience building distributed systems
Nice to Have
Machine Learning fundamentals experience, Large Language Model fundamentals experience, Experience with ML architecture, Experience with model training lifecycles, Experience with model inference lifecycles, Experience optimizing model execution, Experience in machine learning, Experience in data mining, Experience in information retrieval, Experience in statistics, Experience in natural language processing
What You'll Do.
Lead engineers to design training data systems
Build scalable training data systems
Define architecture for high-throughput pipelines
Define roadmap for high-throughput pipelines
Transform customer behavior data
Analyze model behavior
Identify model quality gaps
Translate insights into improvements
Drive design of training data recipes
Drive evolution of training data recipes
Establish experimentation frameworks
Establish measurement frameworks
Validate training signal quality
Ensure data governance
Provide technical leadership
Provide architectural guidance
Lead development of agent-driven workflows
Automate data curation
Automate continuous model improvement
Foster ownership culture
Foster operational excellence
Foster high engineering standards
Collaborate cross-team
Accelerate AI model iteration
How You'll Work.
Team & Collaboration
Cross-team collaboration; Partnering with scientists; Partner organizations
Process & Methodology
Roadmap, Project management
Full Job Description
Join us in building the systems that enable Amazon’s AI models to learn from real-world customer behavior and continuously evolve at massive scale. We are seeking a Software Development Manager to lead the design and development of large-scale training data systems and experimentation frameworks that power Amazon’s next-generation shopping AI. This role leads a team responsible for building distributed systems that transform real customer interactions into measurable learning signals that continuously improve the Amazon shopping experience. As a Software Development Manager, you will define the technical vision and execution strategy for scalable systems that convert live traffic into structured datasets used across model training and evaluation workflows. You will lead engineers in building reliable, production-grade infrastructure while partnering closely with applied scientists to analyze model behavior, identify model quality gaps, and translate those insights into improved training data recipes and experimentation frameworks. This role operates at the intersection of ML infrastructure, data science, and production systems, with end-to-end ownership spanning data ingestion, training signal design, experimentation, and evaluation. You will drive the team’s roadmap, mentor engineers, and collaborate across teams to ensure Amazon’s AI systems can continuously learn from real customer behavior. You will also lead the integration of agent-driven systems that automate data curation, evaluation, and continuous model improvement, helping redefine how large-scale AI training workflows evolve in production. Key job responsibilities Lead a team of engineers to design and build scalable training data systems that enable AI models to continuously learn; Define the architecture and roadmap for high-throughput pipelines that transform customer behavior data to be used across model training and post-training workflows. Partner with scientists to analyze model behavior, identify
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