Machine Learning Scientist

Machine Learning Scientist Rime builds voice AI for enterprises running customer experiences at scale. Our text-to-speech models are purpose-built for high-volume conversational deployments, engineered for the pronunciation accuracy, latency, and deployment flexibility that production environments actually demand. We started from a different premise than the rest of the field: voice AI isn't bottlenecked by model architecture. It's bottlenecked by data. So before we trained a single model, we built our own corpus: full-duplex, studio-quality conversational speech, recorded and annotated by PhD linguists. That's our moat. It's also why enterprises pick Rime when pilots need to convert into production. We're backed by top-tier investors including Unusual Ventures, and we've built a team at the intersection of product, research, and craft. Building voice models is an art. We intend to master it. Role Overview We're hiring a Machine Learning Scientist to push the frontier of speech synthesis and speech understanding at Rime. What You'll Own Design, train, and evaluate speech synthesis models, autoregressive and non-autoregressive. Drive research on full-duplex and half-duplex multi-modal architectures, including unified S2S systems. Choose and iterate on speech representations: neural codecs, semantic tokens, mel features, continuous latents. Build rigorous evaluation, objective and perceptual. Hold the bar on quality and prosodic control. Collaborate with our linguists on TTS frontend behavior so modeling and frontend choices reinforce each other. What We're Looking For Deep familiarity with the speech synthesis literature, contemporary and historical — Tacotron, FastSpeech, VITS, VALL-E, the codec-LM lineage. Opinions on what worked and why. Hands-on training with neural codecs (EnCodec, DAC, Mimi, etc.) and multiple representation choices. Experience with full- or half-duplex multi-modal modeling (Moshi, LLaMA-Omni, streaming S2S). Strong attention to detail on data