Overview In this role you will build expressive, socially intelligent robot interactions. You will advance verbal and non-verbal conversational systems, social dynamics, and long-term relationships between robots, environments, and people. Your work enables trustworthy, engaging robot behavior at scale and across contexts. You will lead projects from concept to deployment and collaborate with cross-functional teams to push HRI and AI frontiers. You will shape how humans experience robots through meaningful, interactive experiences.
Compensation / Benefits- health insurance (medical, dental, vision)
- 401(k) matching
- paid time off
- parential leave
- RSUs
- sign-on payments
Responsibilities- Develop interactive systems using LLMs, multimodal inputs/outputs, and RL from human feedback to enable fluid robot behavior
- Design intelligent conversational systems handling turn-taking, grounding, interruptions, and contextual data from environments and user history
- Integrate perceptual sensor streams (gaze, facial expression, gesture, posture) to inform social context and lifelike interactions
- Create memory and personalization systems for long-term user relationships and environment learning
- Stay current with HRI, NLP, multimodal AI, and cognitive/social science to apply cutting-edge techniques
- Lead technical projects from ideation through production deployment
- Mentor junior scientists and engineers
- Bridge research initiatives with practical engineering implementations
Key requirements- 3+ years building models for business applications
- PhD, or Master's degree with 4+ years in CS, CE, ML or related field
- Experience in patents or publications at top-tier conferences or journals
- Programming experience in Java, C++, Python or related language
- Experience in algorithms/data structures, parsing, numerical optimization, data mining, parallel and distributed computing, HPC
- leadership and mentoring
- cross-functional collaboration
- strong communication
- large language models
- multimodal AI
- reinforcement learning from human feedback
