• Create training environments that elevate the quality ceiling of synthetic data, and provide high-quality reward signals for both off- and on-policy learning. • Design model behaviors that optimize for interpretability and an outstanding user experience. • Train agentic models that autonomously interact with complex structured and unstructured environments, write and prototype code, and navigate multimodal inputs. • Own benchmarking pipelines – assemble high-quality datasets and measure impact of different training parameters or methodologies. • Architect scalable inference strategies that maximize compute efficiency. • Maintain solutions to ensure stable, reliable execution for customers. • Closely collaborate with engineering team to design important intersections of AI and product services such as interfaces, inference tracking, checkpointing, data persistence and caching. • Contribute to engineering culture; mentor peers, evolve patterns, raise the bar on code quality, testing strategy, and documentation.