Senior Research Scientist - Multimodal Agents
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La posizione
Lead research and development of agentic systems spanning planning, tool use, memory, and retrieval for real tasks in design, vision, and language.
Design and optimize reward models and learning loops (RLHF/RLAIF, DPO/IPO) and drive policy optimization across distributed training pipelines.
Build robust evaluation suites, simulate failure modes, and ensure safety, reliability, and scalability of multi-step reasoning.
Advance post-training approaches and MoE architectures while collaborating in a cross-functional environment.
Mentor teammates, share findings, and translate research into high-quality, ship-ready product features.
Partner with safety, product, design, and platform teams to align research with business goals while maintaining rigor and reproducibility.
Design and optimize reward models and learning loops (RLHF/RLAIF, DPO/IPO) and drive policy optimization across distributed training pipelines.
Build robust evaluation suites, simulate failure modes, and ensure safety, reliability, and scalability of multi-step reasoning.
Advance post-training approaches and MoE architectures while collaborating in a cross-functional environment.
Mentor teammates, share findings, and translate research into high-quality, ship-ready product features.
Partner with safety, product, design, and platform teams to align research with business goals while maintaining rigor and reproducibility.
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