Machine Learning Engineer, Data
Machine Learning Engineer, Data & Training Infrastructure
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 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: from the upload interface, audio preprocessing (VAD, ASR), annotation, training data export, to evaluation.
What You'll Own
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 ("speakers X/Y/Z, duration 3–12s, quality > 0.8") 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
What We're Looking For
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.
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.
Why Join Rime
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.
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 To This Job
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 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: from the upload interface, audio preprocessing (VAD, ASR), annotation, training data export, to evaluation.
What You'll Own
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 ("speakers X/Y/Z, duration 3–12s, quality > 0.8") 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
What We're Looking For
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.
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.
Why Join Rime
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.
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 To This Job