We are building visual conversational AI that feels human. Backed by Accel, Lightspeed and South Park Commons.
About the role
A great model is only part of the product. The rest is everything a person touches before, during, and after a conversation with our avatar, along with the tools our researchers rely on to make the next model better. We’re looking for a Product Engineer who can own that surface end to end, from user-facing features and our developer platform to the internal tooling behind our research. You might spend one week on a feature for a launch and the next pairing with a researcher on tooling for an upcoming study. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- A track record of shipping product features end to end. You can walk us through what you built, the tradeoffs you made, and what happened after launch
- Strong software engineering fundamentals in whatever languages you know best. We care more about how you think than which stack you’ve used
- Proficient, everyday use of agentic AI tools, and the judgment to check their work
- Product judgment. You care about how something feels to use, and you know where polish matters and where a quick version will answer the question
- Comfort with production systems: load, rollouts, monitoring, and what to do when something breaks
- Self-drive. Given a problem rather than a spec, you figure out what needs doing and follow through without daily direction
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.
A great model is only...
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