We are building visual conversational AI that feels human. Backed by Accel, Lightspeed and South Park Commons.
About the role
We’re looking for a deeply technical Member of Technical Staff to own RL and post-training for large-scale omni models. This posting is aimed at experienced researchers and engineers who’ve operated at a senior to senior-staff level at big tech or a leading research lab. Everyone at Nuance is MTS — we don’t run title ladders — but we’re hiring people who have already done this work at scale.
What they're looking for
- Significant hands-on experience with RL, RLHF, RLAIF, post-training, alignment, or large-scale fine-tuning for modern foundation models
- Deep understanding of RL/post-training methods: policy optimization, reward modeling, preference optimization, rejection sampling, KL control, evaluation, and data feedback loops
- A track record reasoning about model behavior and training dynamics: reward hacking, unstable rewards, distribution shift, stale policies, mode collapse, over-optimization, noisy preferences, and evaluation mismatch
- Experience with large-scale training or inference systems, including rollout generation, model serving, batching, queueing, GPU utilization, checkpointing, and debugging
- Understanding of omni post-training for real-time audio-video-language interaction: temporal alignment, interruption, emotional response, and multimodal evaluation
- Strong software engineering fundamentals, curiosity, and adaptability to new RL algorithms, model architectures, serving systems, evaluation methods, and research ideas
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’re looking for a...
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