• Design and develop ML infrastructure, tooling, and models to help teams deliver world class experiences • Build internal and external products and platforms to enable teams to incorporate AI into their features and customer facing products • Translate product goals into actionable engineering plans and build scalable, resilient services for data integration and event processing • Help product and development teams understand the data lifecycle and consult with teams on common ML patterns/tradeoffs • Coach and collaborate inside and outside the team to elevate technical standards • Write high-quality, performant, sustainable, and testable code while working in a cloud environment • Monitor the industry landscape, anticipate where technological advances are heading, and ensure Weave stays ahead of the curve • Cut through noise and hype to identify genuine strategic value; advocate for and lead key initiatives that prepare Weave for emerging challenges • Shape company-wide standards for engineering excellence, observability, and reliability in distributed systems • Actively mentor Staff and Senior Engineers across the fellowship, developing the next generation of technical leaders • Elevate architectural thinking across teams through design reviews, documentation standards, and hands-on guidance • Build organizational capability that persists beyond your individual contributions

• Design, develop, and maintain Databricks notebooks and jobs supporting data transformation, analytics, and machine learning workloads. • Develop, integrate, and deploy custom machine learning models using tools such as Azure Machine Learning and MLflow. • Implement data and AI use cases on the Databricks platform, ensuring seamless integration and operational reliability. • Configure and manage external AI models within Databricks Model Serving, including secure management of API keys. • Build scalable asynchronous agents using native Python async patterns to support higher‑concurrency workloads. • Apply CI/CD and DevOps best practices using Azure DevOps to automate deployment and management of data pipelines and ML infrastructure. • Inspect and interpret data, metadata, and database structures using SQL editors and built‑in visualizations. • Collaborate closely with data scientists, analysts, and engineers to translate requirements into production‑ready ML solutions. • Write clean, modular, well‑documented, and testable Python code following established engineering standards.

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