Senior Applied Scientist, Real-Time Conversational AI , AGI

We are looking for a Senior Applied Scientist to help drive the research and development of real-time multimodal conversational AI. You will contribute across two focus areas: advancing foundation models for speech and audio, and building the post-training systems (reward modeling, reinforcement learning) that shape natural, human-like conversational behavior.

You will own a significant research area and contribute across the full model lifecycle - from pre-training and architecture design through post-training alignment and real-time deployment. You will work at the frontier of what's possible in conversational AI, with the compute, data, and runway to pursue problems that few teams in the world have the resources to tackle.

As a Senior Scientist, you will drive the technical execution of your research area, contribute to the team's roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment.

Key job responsibilities

What You'll Do

Foundation Model Scaling

• Help build and train large-scale multimodal foundation models for real-time speech and audio generation, from architecture design through production-scale training
• Advance the scaling and efficiency of conversational models, including the relationship between data, model size, and real-time performance
• Design model architectures informed by hardware constraints and inference requirements, working with inference engineers to ensure models are servable from inception
• Develop training methodologies for multimodal models that jointly process and generate speech, language, and audio in real-time streaming contexts
• Contribute to the state of the art on efficient architectures and training methods for conversational AI at scale

Post-Training & Reinforcement Learning

• Design and build reward models and reward functions for speech systems - capturing naturalness, fluency, conversational quality, and real-time responsiveness
• Develop and apply reinforcement learning methods to shape conversational behavior - teaching models natural timing, responsiveness, and fluid interaction
• Build parts of the post-training pipeline from SFT through RL alignment, optimized for real-time multimodal outputs rather than text-only generation
• Design evaluation frameworks that capture the quality dimensions unique to real-time conversation (latency sensitivity, audio quality, prosody, interaction naturalness)

Real-Time Perception & Generation

• Advance the team's capabilities in real-time perception - the ability of the model to process incoming audio/speech while simultaneously generating responses
• Develop techniques for natural interactive systems where the model handles concurrent input and output with human-like timing
• Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets