Nuance Labs

Technology

NuanceResearchFellow

$200–250k Seattle, Washington, United States INTERNSHIP
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Nuance Research Fellow at Nuance Labs. Skills: Multimodal LLMs, Generative modeling, Real-time AI, Audiovisual AI. Own research problem. Frame research problem”

Industry & Context.

Technology
Problems you'll solve

Research problem framing; Experiment design; Data analysis; Scientific rigor; System integration; Model training; Model debugging; Model interpretation; Evaluation harness building; Benchmark building; Analysis tooling; Prototype development

What They're Looking For.

Must Have

Working knowledge of PyTorch, Deep learning knowledge, Train a model, Debug a training run, Reason about loss level, First-author paper tier 1 venue, Genuine interest in joining Nuance full-time

Nice to Have

Multimodal LLMs experience, Omni models experience, Audio-language models experience, Video-language models experience, Speech generation experience, Real-time interactive agents experience, Flow matching experience, Diffusion models experience, Rectified flows experience, Autoregressive generation experience, Neural codecs experience, Generative modeling methods experience, Multiple tier 1 publications, Paper significant attention, Olympiad medals or finalist, Codeforces grandmaster, ICPC world finals, Putnam fellow, Kaggle grandmaster, Open-source contributions ML frameworks, Independent projects made faster, Independent projects made smaller, Independent projects made better

What You'll Do.

Frame research problem

Integrate into Nuance stack

Work on Multimodal LLM systems

Explore generative modeling techniques

Adapt generative modeling techniques

Reproduce key results

Turn ideas into experiments

Instrument training runs

Interpret training runs

Design evaluation runs

Instrument evaluation runs

Debug evaluation runs

Interpret evaluation runs

Build evaluation harnesses

Build analysis tooling

Turn prototypes into systems

Work with senior researchers

Work with senior engineers

How You'll Work.

Team & Collaboration

Senior researchers; Senior engineers

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 The Nuance Research Fellowship is a 3-month engagement for early-career researchers who want to work at the frontier of Multimodal LLMs, generative modeling, and real-time audiovisual AI. The program is open to current PhD students (on internship, leave, or in their final stretch) and recent graduates from BS, MS, or PhD programs. As a fellow, you’ll own a real research problem inside one of our core workstreams: pretraining, post-training, RL, evaluation, data, multimodal modeling, generative modeling, or inference. Depending on your strengths, this could mean training omni models from scratch, improving real-time audio-video-language reasoning, building evals for full-duplex interaction, or exploring model families such as flow matching and diffusion for controllable, high-fidelity generation. This is designed as a

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