Research Engineer, Voice
Inflection AI - Palo Alto, California, United States
Posted May 12, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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Where they hire
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About this role
Research Engineer, Voice Palo Alto, California, United States About Inflection AI Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We're shaping the future of AI by combining emotional intelligence (EQ) and raw intelligence (IQ) to elevate people's potential. Inflection AI created Pi, the world's first emotionally intelligent AI, to help people work through decisions, emotions, and challenges. Pi is a personal AI agent powered by Inflection AI's foundation model, proving that AI can be personal, empathetic, and contextually aware. About the Role We're looking for a Member of Technical Staff (MTS), Research Engineer focused on voice and audio to help advance the spoken intelligence behind Pi. In this role, you'll work at the intersection of research and production-developing, training, and shipping neural models across the full spectrum of voice: speech synthesis, recognition, audio generation, and real-time spoken dialogue. You'll collaborate closely with ML engineers, product teams, and infrastructure to turn cutting-edge ideas in areas like neural audio codecs, diffusion-based TTS, and multimodal foundation models into the natural, expressive voice experiences that millions of Pi users interact with every day. What You'll Do - Research, develop, and optimize neural models for voice and audio-including text-to-speech, automatic speech recognition, audio generation, and spoken dialogue systems. - Build and maintain production-grade training and inference pipelines for voice models, with close attention to latency, naturalness, and scalability. - Run experiments end-to-end: data curation, model architecture design, training, evaluation, and ablation studies. - Collaborate with ML engineers, product teams,
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