Build and operate research infrastructure like evaluation frameworks, RL training systems, experiment tracking, and visualization tools. Partner directly with ML researchers to identify bottlenecks, ensure high adoption, and accelerate research velocity. Requires strong software engineering skills and Python/Rust proficiency.
Conducts research on visual perception, multimodal learning, and large-scale AI model training. Designs architectures, builds datasets and evaluations, and collaborates on frontier models. Requires ML expertise, Python proficiency, and experimental rigor.
Designs and implements methods for sourcing, curating, and analyzing large-scale pre-training datasets for AI models, blending research with production-grade data engineering. Requires Python proficiency, deep learning frameworks, and strong ML fundamentals.
Conducts post-training research for AI models, designing data collection strategies, developing labeling pipelines, modeling human preferences, and iterating on evaluations to improve model alignment, reasoning, and helpfulness. Requires strong Python skills, ML framework proficiency, and experimental rigor.
Develops and tunes post-training recipes for AI models, iterates on evaluations, debugs configurations, scales methodologies, and publishes research to advance collaborative intelligence. Requires Python proficiency, deep learning frameworks, and strong ML fundamentals.
Build and operate research infrastructure like evaluation frameworks, RL training systems, experiment tracking, and visualization tools. Partner directly with ML researchers to identify bottlenecks, ensure high adoption, and accelerate research velocity. Requires strong software engineering skills and Python/Rust proficiency.
Conducts research on visual perception, multimodal learning, and large-scale AI model training. Designs architectures, builds datasets and evaluations, and collaborates on frontier models. Requires ML expertise, Python proficiency, and experimental rigor.
Designs and implements methods for sourcing, curating, and analyzing large-scale pre-training datasets for AI models, blending research with production-grade data engineering. Requires Python proficiency, deep learning frameworks, and strong ML fundamentals.
Conducts post-training research for AI models, designing data collection strategies, developing labeling pipelines, modeling human preferences, and iterating on evaluations to improve model alignment, reasoning, and helpfulness. Requires strong Python skills, ML framework proficiency, and experimental rigor.
Develops and tunes post-training recipes for AI models, iterates on evaluations, debugs configurations, scales methodologies, and publishes research to advance collaborative intelligence. Requires Python proficiency, deep learning frameworks, and strong ML fundamentals.