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.
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.
Nice to have
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.
What We Offer
Competitive base + meaningful early-stage equity
Remote-friendly
Visa sponsorship available
Access to a proprietary, full-duplex, studio-quality conversational speech corpus
Compute and tooling to do the work
Direct influence on the future of voice AI
At Rime, we...
Are outliers
Cut through the hype to focus on the craft
Move fast with agency and freedom
Maintain a growth mindset, finding joy in the struggle
Do the right things, knowing that it'll lead to making money
If that sounds like you too, you'll be a great fit for Rime!