Rime Labs

Role Description We're hiring a Machine Learning Engineer to own the operational data pipeline end-to-end. The role requires "T-shaped" expertise: depth in data and orchestration fundamentals, and the ability to coordinate everything that touches the data. - End-to-end audio annotation pipeline: Currently some stages exist as prototypes; productionizing and rebuilding them is work that’s currently in flight. - Quality systems: Automated tooling to catch annotation errors, alignment drift, and silent regressions before training runs. - Dataset versioning and experimenter tooling: The model team will want to subset the vetted pool into reproducible training manifests. The query interface, manifest format, and lineage tracking are all yours. - Linguist- and annotation-team-facing tooling: Annotation UI, PM workflow for project management, QC dashboards. - Pipelines for full- and half-duplex training data. Qualifications - Strong software engineering fundamentals: Python, distributed systems, comfort across the stack. - Database design fluency: You reach for the right schema and have operated Postgres or similar in production. - Production data pipelines on cloud-native infrastructure (GCP preferred). Our data stack is currently GCP-dominant. - Operational comfort: Containers, CI/CD, IAM, cost-aware infrastructure choices, etc. - Strong attention to detail on data quality. - Comfort being out of your depth at the boundary: You'll sometimes debug code you didn't write in tools you don't use daily. You should find this energizing, not threatening. - Bias toward building the abstractions so the modeling team doesn't stay stuck doing data work by hand. Requirements - Nice to have: Multilingual data pipeline experience. - Audio DSP, signal processing, or speech recognition background. - Large-scale training infra (FSDP, DeepSpeed, Ray). - Annotation tooling and human-in-the-loop systems. - Comfort working close to research teams. Benefits - Build the data infrastructure behind a category-defining voice AI company. - The pipelines you build determine what models we can train. - Meaningful equity upside. - High ownership, high standards, low bureaucracy. - 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.

Role Description We're hiring a Machine Learning Scientist to push the frontier of speech synthesis and speech understanding at Rime. - 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. Qualifications - 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. Requirements - 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. Benefits - 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

Role Description We’re hiring a Machine Learning Engineer to own inference for Rime’s models in production. Voice is unforgiving because every millisecond shows up in the conversation. You’ll build the systems that turn our models into the lowest-latency, highest-throughput, most reliable speech systems in the industry. What You’ll Own - In-house real-time speech-first inference stack: model compilation, kernel optimization, batching strategy, streaming output, the path from checkpoint to first-audio-byte. - Latency systems: TTFB targets across regions, KV cache management, speculative decoding, scheduler design. - Deployment flexibility: cloud, on-prem, BYOC (SageMaker, Connect), the packaging and runtime story across heterogeneous environments. - Inference for full- and half-duplex models, including streaming codec encoding and decoding. Qualifications - Strong software engineering fundamentals: Rust, Python, C++/CUDA welcome, distributed systems, comfort across the stack. - Hands-on experience serving ML models at scale in production, ideally for low-latency or streaming workloads. - Deep familiarity with inference engines (vLLM, SGLang), SDKs (TensorRT, ONNX, CUDA Graphs, Triton), etc. - Working knowledge of speech synthesis and/or speech recognition techniques. - Familiarity with multiple speech representations (neural codecs, semantic tokens, mel/STFT) and how they shape inference cost. - Experience optimizing transformer or autoregressive model inference: KV caching, quantization, paged attention, speculative decoding. - Willing to roll up your sleeves on unglamorous performance work — flame graphs, NSight traces, kernel tuning, paired with the agency to build the abstractions so the team doesn’t stay stuck doing it by hand. - Bias toward shipping. Requirements - CUDA kernel authoring or Triton experience (nice to have). - GPU profiling and microarchitecture intuition (H100, A100, L40S, Blackwell) (nice to have). - Experience with parallel model training infrastructure (nice to have). - Multi-tenant inference scheduling and fairness (nice to have). - Comfort working close to research teams and influencing model architecture choices for inference-friendliness (nice to have). Benefits - Build the inference stack behind a category-defining voice AI company. - Direct collaboration with founders, including a CEO with a Stanford computational linguistics PhD who takes latency as seriously as you do. - The systems you build determine what experiences our customers can deploy. - 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. Company Description 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 Description We are hiring our first Go-to-Market Engineer to build the systems that turn our product, content, and outbound motion into a repeatable revenue engine. You will be Rime's first dedicated GTM systems builder, reporting to the Head of Marketing and partnering daily with our four AEs, sales leadership, our incoming DevRel, and our product teams. Your job is to design and ship the infrastructure that makes every other GTM hire more effective. What you'll own - Pipeline and signal infrastructure: - Build and maintain Rime's account architecture across our GTM segments. - Design the signal layer that surfaces the right accounts to the right AE at the right time. - Build and maintain the golden list: the accounts where, if we got a magical intro tomorrow, we would be thrilled. - AE and seller enablement: - Automate the manual research, prep, and follow-up work that today eats AE time. - Build the workflows that let four AEs operate like eight. - Partner with sales leadership to instrument the funnel. - Marketing and growth systems: - Extend our existing PLG onboarding flow in HubSpot. - Own the systems behind our AEO and SEO programs. - Build and maintain attribution and reporting infrastructure. - Cross-functional infrastructure: - Partner with our incoming DevRel hire to instrument community signals, event ROI, and developer activation. - Partner with our incoming Product Marketing Manager on launch infrastructure. - Partner with operations on the systems that bridge marketing, sales, and finance. Qualifications - 3 to 5 years of hands-on experience in a GTM, RevOps, growth, or marketing ops role where you owned systems, not just executed plays. - Technical fluency: comfortable in Clay, n8n, or similar workflow tools; can write SQL, work with APIs, and read enough Python or TypeScript. - Commercial bias: think in terms of pipeline, conversion, and revenue impact. - Systems thinking: map customer journeys, design data flows, and reach for repeatable systems. - Hacker mentality: resourceful and creative with available tools. - Comfort with ambiguity: structure fuzzy problems and ship before requirements are perfect. Requirements - Experience at a developer-first or infrastructure company. - Experience instrumenting AEO, SEO, or LLM citation tracking. - Experience supporting a sales-led and self-serve motion at the same time. Logistics - Remote within the US only. - Full-time, with competitive cash, meaningful equity, and full benefits. - You will report directly to the Head of Marketing and sit on the broader GTM team. Company Description 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