Nuance Labs
Technology
MemberofTechnicalStaff—PretrainingInfra
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Member of Technical Staff — Pretraining Infra at Nuance Labs. Skills: Distributed training infrastructure, Large-scale AI, GPU execution. Own distributed training stack. Design 0→1 system”
What You'll Achieve.
Impact what models we can train; Impact how fast research can iterate; Impact how reliably we scale
Industry & Context.
Debugging; Troubleshooting; Performance debugging
What They're Looking For.
Must Have
Hands-on experience running large-scale distributed training jobs, Experience at hundreds of GPUs minimum, Deep understanding of distributed training mechanics, Understanding of GPU communication and performance debugging, Practical experience with at least one major large-scale training stack, Understanding of omni or multimodal training challenges, Software engineering fundamentals, Curiosity, Adaptability to new model architectures, training frameworks, hardware constraints, and research ideas
Nice to Have
1,000+ GPUs a plus, Prior 0→1 experience building large-scale training infrastructure, Deeply modifying core training frameworks, runtimes, checkpointing, or debugging systems, Experience training large omni or multimodal models, Publications or substantial open-source contributions in ML systems, distributed systems, HPC, GPU performance, or training infrastructure
What You'll Do.
Own distributed training stack
Build core training runtime
Manage distributed execution
Monitor training jobs
Optimize large-scale training performance
Optimize parallelism strategy
Optimize GPU communication
Optimize memory usage
Optimize data throughput
Optimize end-to-end training efficiency
Build infrastructure for omni training workloads
Load high-throughput data
Handle temporal alignment
Handle variable sequences
Synchronize multimodal data
Train memory-efficiently
How You'll Work.
Team & Collaboration
Research; Systems; GPU-scale execution
Process & Methodology
Job orchestration
Full Job Description
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 Series A company ($60M raised) backed by Lightspeed, Accel, South Park Commons, NVentures, and Define Ventures, with PhDs from MIT, UW, Oxford, CMU, and Johns Hopkins, and industry experience from Apple, Meta, Amazon AGI, and Discord. 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
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