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• Design multimodal, multitask datasets that teach world models new capabilities — deciding what data to collect, generate, or curate and measuring its effect on model behavior • Run controlled training experiments to understand how data composition drives model performance across tasks and domains • Build and operate large-scale pipelines for synthetic data generation, filtering, and quality control • Define evaluations and benchmarks that measure whether our models are actually improving at the things that matter • Partner with product and creative teams to translate target behaviors and capabilities into concrete data strategies

• Help world models train faster and run more efficiently. • Profile, optimize, and rearchitect systems that turn research ideas into models that run at scale and in real time. • Optimize training throughput across large GPU clusters. • Design and maintain distributed training infrastructure. • Profile and accelerate inference pipelines for real-time multimodal generation. • Optimize and scale training infrastructure to improve efficiency and reliability. • Contribute to the entire stack, from low-level kernel optimizations to high-level model design.

• Participate in developing cutting-edge machine learning and data analytic solutions in healthcare • Participate in the development, research, evaluation, and deployment of machine learning methodologies for real-time clinical decision systems, patient monitoring, and alerting solutions • Achieving long-term and ambitious product development goals while identifying intermediate milestones • Being independent problem solvers while operating in a collaborative environment