Machine Learning Scientist

Rime

active

Official active full-time posting for Machine Learning Scientist at Rime, located in United States.

Role facts

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.

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.
Working knowledge of TTS frontend (G2P, normalization, prosody) and experience working with linguists.
Strong PyTorch fundamentals. Comfortable with training loops, distributed training, model internals.
PhD or equivalent research experience in speech, audio, ML, or computational linguistics or a track record that makes the credential irrelevant.

Multilingual TTS experience.
Background in prosody or paralinguistics.
Published work in speech, audio, or core ML venues.
Experience taking research models to production: quantization, distillation, streaming inference.
WHY JOIN RIME
Category-defining voice AI infrastructure, not incremental research deltas.
Direct collaboration with founders, including a CEO with a Stanford computational linguistics PhD.
Real impact on company trajectory.
Meaningful equity upside.
High ownership, high standards, low bureaucracy.