We are building visual conversational AI that feels human. Backed by Accel, Lightspeed and South Park Commons.
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.
What they're looking for
- Proven experience building and operating large-scale data pipelines in production — you've processed data at a scale where naive approaches break
- Strong proficiency with distributed data processing frameworks — Spark, Ray, Dask, or similar — and a clear sense of when to use each
- Solid software engineering fundamentals: you write clean, testable, maintainable code and understand why that matters when pipelines run unattended at scale
- Experience with multimodal data (video, audio) is a strong plus — understanding of formats, codecs, and processing libraries (FFmpeg, decord, etc.)
- Familiarity with ML data pipelines specifically — understanding of how data quality and format affect model training
- Ability to move fast: you can take a prototype script from a researcher and ship a production version in days, not weeks
More about this role
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.
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.
Model quality is...
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