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 MTS to own distributed training infrastructure for large-scale omni model pretraining. This role sits at the intersection of research, systems, and GPU-scale execution — building the training stack from 0→1 and scaling it: distributed execution, parallelism, GPU communication, data loading, checkpointing, observability, and debugging.
What they're looking for
- Hands-on experience running large-scale distributed training jobs across large GPU clusters, experience at hundreds of GPUs minimum, 1,000+ GPUs a strong plus
- Practical experience with at least one major large-scale training stack such as Megatron, PyTorch FSDP, DeepSpeed, or equivalent internal infrastructure
- Understanding of omni or multimodal training challenges, especially audio/video/language data, long temporal context, variable sequence lengths, modality-specific bottlenecks, and high-throughput dataloading
- Strong software engineering fundamentals, curiosity, and adaptability to new model architectures, training frameworks, hardware constraints, 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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