Member of Technical Staff — Pretraining Infra (Experienced)
Position Summary & Responsibilities
About Nuance Labs
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.
How Nuance Differentiates
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.
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.
Our models are omni from the ground up (audio, video, language, real-time full-duplex), which introduces systems challenges beyond standard LLM training: multimodal synchronization, long temporal context, variable sequence lengths, and tight memory/throughput constraints.
High ownership. Direct impact on what models we can train, how fast research can iterate, and how reliably we scale.
What You’ll Own
- Own the distributed training stack for omni model pretraining, from 0→1 system design to 1→10 scaling across large GPU clusters.
- Build and operate the core training runtime: job orchestration, distributed execution, checkpointing, recovery, monitoring, and debugging for long-running training jobs.
- Optimize large-scale training perf
Key Role Competencies & Evaluation Highlights
Evaluators scrutinize rapid product iteration, advanced AI/ML fluency, and the ability to build intuitive, user-centric conversational intelligence interfaces.
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