Sr Applied Scientist, Ring AI
Posted Updated
Are you a passionate scientist who wants to build AI agents that make a real difference in people's lives? At Ring, our mission is to make neighborhoods safer, and we believe agentic AI will change how customers interact with their homes and communities. You'll invent agents that reason about real-world situations, take meaningful action, and keep customers in control, and then you'll see them reach millions of households.
As an Applied Scientist, you'll work with talented peers to push the frontier of agentic AI. You'll build agents that turn the multimodal signals captured by Ring devices into understanding and action. You'll tackle open problems in planning, tool use, learning from feedback, and reliability, taking ideas from research all the way to deployment at scale. You'll collaborate with teams across Amazon to advance the science of customer experiences through highly optimized, integrated hardware and software platforms.
Key job responsibilities
- Design, develop, and deploy LLM-based agents that plan and carry out multi-step tasks for customers using tools, services, and device data.
- Advance the state of the art in agent capabilities such as planning, tool use, memory, and learning from feedback (e.g., RL and agent fine-tuning), and publish where appropriate.
- Partner with engineering, product, and science teams to turn research into agent-driven experiences that help keep homes and neighborhoods safer.
As an Applied Scientist, you'll work with talented peers to push the frontier of agentic AI. You'll build agents that turn the multimodal signals captured by Ring devices into understanding and action. You'll tackle open problems in planning, tool use, learning from feedback, and reliability, taking ideas from research all the way to deployment at scale. You'll collaborate with teams across Amazon to advance the science of customer experiences through highly optimized, integrated hardware and software platforms.
Key job responsibilities
- Design, develop, and deploy LLM-based agents that plan and carry out multi-step tasks for customers using tools, services, and device data.
- Advance the state of the art in agent capabilities such as planning, tool use, memory, and learning from feedback (e.g., RL and agent fine-tuning), and publish where appropriate.
- Partner with engineering, product, and science teams to turn research into agent-driven experiences that help keep homes and neighborhoods safer.