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 quality. You notice when an annotation pipeline is silently degrading or when an eval set has leakage.

• Willing to roll up your sleeves on unglamorous data and training work — paired with the agency to build pipelines so the team isn't stuck doing it by hand.