Machine Learning Scientist

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!