We are building visual conversational AI that feels human. Backed by Accel, Lightspeed and South Park Commons.
About the role
We can train a great model. The next problem is making it fast enough to actually use in a real-time conversation — and that gap is enormous. A model that responds in 3 seconds is a demo. A model that responds in under 500ms is a product.
What they're looking for
- Significant hands-on experience with LLM inference optimization — you’ve shipped work on KV caching, memory layout, attention kernels, or batching strategies in a production or high-traffic research context
- Proven proficiency with inference serving frameworks — vLLM, SGLang, TensorRT-LLM, or similar — including going well beyond default configurations and adapting them to non-standard workloads
- Experience optimizing diffusion model inference (latency reduction, step distillation, caching, or kernel-level work)
- Strong Python and PyTorch skills, comfort reading and writing CUDA or Triton kernels is a significant plus
- A systematic approach to profiling and optimization — you measure first, then optimize
- Familiarity with speculative decoding or other inference-time acceleration techniques
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.
We can train a great...
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