Principal Applied Scientist, Real-Time Conversational AI , AGI

Key job responsibilities

• Foundation Model Scaling
• 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 modes, 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
• 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
• Design 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
• Real-Time Perception & Generation
• Advance the team's capabilities in real-time perception
• Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets

Basic Qualifications

• PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field
• 10+ years of industrial or academic work building speech recognition and natural language processing systems (like commercial speech products or government speech projects)
• Experience with large-scale foundation model training
• Experience with multimodal model architectures that jointly process or generate across speech, text, and audio modalities
• Strong understanding of transformer architectures and their application to speech/audio domains
• Publishation record at top-tier venues (NeurIPS, ICML, ICLR, Interspeech, ICASSP, ACL, or equivalent)
• Demonstrated Principal/Staff+ scientific leadership — setting research direction, mentoring researchers, influencing multi-team decisions

Preferred Qualifications

• Hands-on experience building real-time AI systems — speech, audio, or video
• Track record with post-training methods: reinforcement learning, reward modeling, RLHF/RLAIF, or alignment techniques applied to generative models
• Experience with real-time interactive systems — models that handle concurrent input and output
• Experience building speech-to-speech or audio-to-audio generative models (codec models, autoregressive audio generation)
• Experience designing reward models or reward functions specifically for speech/audio quality, naturalness, or conversational behavior
• Familiarity with hardware-informed model design — understanding how architecture choices affect inference latency, memory, and cost
• Background in speech recognition, speech synthesis (TTS), or speech enhancement
• Experience shipping research to production at scale — models serving real users, not just benchmark results
• Contributions to open-source speech/audio ML systems or widely used research codebases
• History of mentoring and growing research teams from individual contributors to technical leaders