# Machine Learning Scientist - Company: [Rime]() - Location: United States - Remote: Yes - Team: Modeling - Employment type: Full Time - Posted: May 15, 2026 ## Job description # **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! ## Apply [Apply on Rime]() Canonical job page: