• Design and ship productionized ML pipelines, from raw data and ETL through model inference and orchestration • Build and deploy ML-powered APIs and services that support real user-facing features • Own the full lifecycle of ML systems: experimentation, deployment, monitoring, and iteration • Develop ML systems for personalization, search, and recommendations • Work with embedding-based systems and vector representations (text, images, or multimodal data) • Measure results rigorously and iterate based on real-world performance • Create and maintain data pipelines and internal tooling • Deploy and operate ML systems in production (AWS, containerization, EC2, service orchestration) • Ensure systems are scalable, reliable, and observable • Monitor model and system performance and improve robustness over time • Maintain a high standard for code quality, testing, and system design • Collaborate closely with leadership and peers to shape technical direction • Move quickly while balancing correctness, reliability, and long-term maintainability