Sr. Software Dev Engineer, SageMaker AI

Join us in building the future of AI-powered data preparation with SageMaker, where we're revolutionizing how organizations ensure data quality for their machine learning initiatives. As part of a strategic initiative to create next-generation data quality and evaluation systems, you'll work at the intersection of latest AI and foundational ML infrastructure. The AI data labeling market is exploding — $3.2B in 2026, projected $25B by 2028 — and the bottleneck to better AI is no longer compute, it's high-quality labeled data at scale. We're building the platform that solves this: auto-labeling with statistical quality guarantees, LLM-as-judge evaluation, and human verification — all unified under one managed service. This is an opportunity to be part of a team launching innovative AI-powered data preparation products from the ground up. You'll architect systems that produce training data at human quality and machine scale — where LLMs label, humans verify, and the system continuously improves from every correction. The role offers high visibility with AWS leadership and the chance to shape products that will transform how businesses prepare and govern their ML data. We're seeking Sr. SDE who thrives in a fast-paced, collaborative environment and isn't afraid to tackle seemingly impossible challenges. You'll build rock-solid, highly-secure software at world-class scale that combines auto-labeling, human-in-the-loop workflows, and LLM-as-Judge techniques to deliver data quality improvements—while partnering closely with ML science teams to push the boundaries of what's possible.

Data Preparation Platform: Design and deliver core components of data preparation journey to customize and fine-tune LLMs in SageMaker, designing systems that provide customers with high-quality, reliable data for their ML workflows.

Drive Innovation in Data Preparation: Build and scale systems leveraging auto-labeling and LLM-as-Judge techniques to automatically detect, diagnose, and remediate data quality issues.

Agent & Model Quality: Establish quality standards and evaluation frameworks for AI agents and models, implementing continuous improvement processes.

Human-in-the-Loop Services: Lead the evolution of our HITL suite, enabling seamless human feedback loops for data labeling, annotation quality assurance, and ground truth generation.

Technical Leadership: Mentor engineers, drive design reviews, and raise the engineering quality bar across the team. Influence technical direction without formal authority.

Architecture & Strategy: Make high-judgment architectural decisions across distributed systems, data processing, and ML infrastructure. Own the technical roadmap for your area.