Overview In this Applied Scientist role, you build and deploy LLM-based agents that reason about real-world home and neighborhood scenarios to improve safety. You will work with cross-functional teams to translate research into scalable agent-driven experiences for Ring devices and services. Expect to tackle planning, tool usage, memory, and learning-from-feedback challenges, with deployment at scale in consumer environments. This is a chance to shape agentic AI that reaches millions of households and enhances customer experiences.
Compensation / Benefits- health insurance (medical, dental, vision)
- 401(k) matching
- paid time off
- parenal leave
- RSUs (restricted stock units)
- sign-on payments
Responsibilities- Design, develop, and deploy LLM-based agents that plan and execute multi-step tasks for customers using tools, services, and device data
- Advance agent capabilities such as planning, tool use, memory, and learning from feedback (RL, fine-tuning) and publish where appropriate
- Collaborate with engineering, product, and science teams to turn research into agent-driven experiences that improve home and community safety
Key requirements- PhD or equivalent research experience
- Hands-on experience building LLM-based agents or agentic systems
- Strong programming skills in Python
- Experience with PyTorch
- Track record of taking ML research from prototype to production or publication
- collaboration across cross-functional teams
- strong communication
- problem solving and initiative
- LLM-based agents
- agentic systems
- multimodal LLMs / vision-language models
