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Description

Amazon seeks an Software Engineering Domain Expert to provide strategic oversight of human-in-the-loop and model-in-the-loop data pipelines that power next-generation AI solutions. This role sits at the intersection of intellectual rigor and technological innovation — blending deep software development expertise with advanced analytical skills to ensure data excellence at every stage of the Model Training pipeline. The Domain Expert owns high-quality data output from end to end, translates training data needs into scalable engineering solutions, and fosters iterative development practices that raise the bar for the entire team. Beyond individual technical contribution, this role builds organizational capability through hands-on mentorship of junior engineers and strategic guidance on data quality standards. The right candidate thrives in ambiguity, brings clarity to difficult problems, and delivers measurable impact on the effectiveness and reliability of AI systems that serve millions of customers.

Key job responsibilities
• Serve as a trusted domain advisor to cross-functional teams, providing strategic direction and specialized problem-solving support
• Champion domain knowledge sharing across multiple channels and teams to maintain data quality excellence and standardization
• Drive collaborative efforts with science teams to optimize output of complex data collections in your domain expertise, ensuring data excellence through iterative feedback loops
• Foster team excellence through mentorship and motivation of peers and junior team members
• Make informed decisions on behalf of our customers, ensuring that selected code meets industry standards, best practices, and specific client needs
• Collaborate with AI teams to innovate model-in-the-loop and human-in-the-loop approaches, to ensure the collection of high-quality data, safeguarding data privacy and security for LLM training, and more.
• Stay abreast of the latest developments in how LLMs and GenAI can be applied to your area of expertise to ensure our evaluations remain cutting-edge.
• Develop and write demonstrations to illustrate "what good data looks like" in terms of meeting benchmarks for quality and efficiency
• Provide detailed feedback and explanations for your evaluations, helping to refine and improve the LLM's understanding and output