Principal Applied Scientist, Real-Time Conversational AI , AGI

About the role

We are looking for a Principal Applied Scientist to drive the research and development of real-time multimodal conversational AI. You will operate 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 work at the frontier of conversational AI with the compute, data, and runway to pursue problems few teams can tackle. As a Principal Scientist, you will set technical direction, influence the broader roadmap, and ensure models are designed for real-time production deployment from inception.

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, collaborating 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.
  • Build 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.

Qualifications

Basic Qualifications

• PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field.
• 10+ years of industrial or academic experience building speech recognition and natural language processing systems (e.g., commercial speech products or government speech projects).
• Demonstrated experience training large-scale foundation models—hands-on involvement in scaling exercises, not just using pre-trained models.
• 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.
• Publication 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 (e.g., codec models, autoregressive audio generation).
• Hands-on design of reward models or reward functions specifically for speech/audio quality, naturalness, or conversational behavior.
• Experience with distributed training at scale—including parallelism strategies, training stability, and curriculum design.
• 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.

Pay

Base salary ranges for this position:

• Sunnyvale, CA: $228,700 - $309,400 USD annually
• Boston, MA: $198,900 - $269,000 USD annually
• Bellevue, WA: $198,900 - $269,000 USD annually
• Seattle, WA: $198,900 - $269,000 USD annually

Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on experience, qualifications, and location.

Benefits

Amazon offers comprehensive benefits including:

• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, and optional supplemental life plans).
• Employee Assistance Program (EAP), Mental Health Support, and Medical Advice Line.
• Flexible Spending Accounts.
• Adoption and Surrogacy Reimbursement coverage.
• 401(k) matching.
• Paid time off and parental leave.

Learn more about our benefits at amazon.jobs/en/benefits.