Member of Technical Staff — ML Infra (Data)
Position Summary & Responsibilities
About Nuance Labs
Nuance Labs is building photorealistic, real-time AI avatars with emotional intelligence: a full-duplex audiovisual system that can listen, speak, react, interrupt, and respond like a real person.
We're a research company, with PhDs from MIT, UW, Oxford, CMU, and Johns Hopkins, and industry experience from Apple, Meta, Amazon AGI, and more. Backed by Accel, Lightspeed, South Park Commons, and NVIDIA, we combine frontier research with ruthless engineering needed for consumer-grade, real-time systems. The team is small, the work is real, and the problems are unsolved.
How Nuance Differentiates
Most conversational AI avatars today are hacks — a face slapped on a speech-to-speech pipeline, stuck in the uncanny valley: emotionless, mechanical, one-turn-at-a-time. Current systems take 2–5 seconds to respond; natural conversation requires sub-500ms. That's a 10x improvement, and it demands rethinking the entire stack.
That rethinking starts with full-duplex: an AI that listens and speaks simultaneously, perceives emotion in real time, and responds with a face that actually reflects it. It's an extremely hard problem, and we're developing foundation models designed for it from the ground up.
About the Role
Model quality is ultimately a data problem. The best architecture and the best training run can't outrun bad, slow, or poorly curated data — and at the scale we're operating, the difference between a good data pipeline and a great one shows up directly in the model.
We're looking for someone who lives and breathes data at scale. You know how to build pipelines that are fast, reliable, and maintainable — and you're just as comfortable taking a researcher's messy processing script and turning it into something that runs on petabytes as you are designing a new pipeline architecture from scratch. Research moves fast here, and the ability to productionize quickly without losing fidelity is the core skill.
Our data is multimodal — video, audio, and text — and the processing requirements are demanding: high throughput, low error rates, and strict quality filters. There's a lot of interesting engineering work here, and the impact is direct and measurable.
What You'll Do
- Design, build, and operate large-scale data pipelines for ingestion, processing, filtering, and curation of multimodal training data (video, audio, text)
Key Role Competencies & Evaluation Highlights
Evaluators scrutinize rapid product iteration, advanced AI/ML fluency, and the ability to build intuitive, user-centric conversational intelligence interfaces.
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