Member of Technical Staff — RL Research; PhD Grad

Member of Technical Staff — RL Research (New PhD Grad)

Seattle, Washington

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 Discord. 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 deeply technical Member of Technical Staff to own RL and post‑training for large-scale omni models. This posting is aimed at researchers who are completing — or have recently completed — a PhD and want to do their best work at a fast-moving frontier lab.

This role is broader than a traditional RL algorithm role. You’ll be expected to understand modern post‑training methods and help build the infrastructure needed to run them  work spans RL method development, rollout generation, reward modeling, policy optimization, evaluation, data feedback loops, serving, observability, and distributed execution.

You’ll help build Nuance’s RL/post‑training stack from 0→1 and scale it from 1→10. That means turning rapidly evolving research ideas into reliable training systems: defining the abstractions, choosing or modifying frameworks, wiring together rollout workers and trainers, building reward/evaluation loops, debugging failure modes, and making the system fast enough for researchers to iterate.

For Nuance, post‑training is not limited to text. Our models are omni from the ground up: audio, video, language, and real‑time full‑duplex interaction. We need RL and post‑training methods that improve interactive behavior, timing, interruption, emotional response, audiovisual coherence, and real‑time conversational quality.

This is a high‑ownership role with direct impact on how Nuance models improve after pretraining — and a place to grow fast alongside people who’ve built these systems before.

• Build Nuance’s RL/post‑training stack from 0→1: rollout generation, policy optimization, reward/reference model serving, data feedback loops, evaluation, checkpointing, observability, and debugging.
• Develop and scale post‑training methods such as PPO, GRPO, DPO, rejection sampling, RLHF/RLAIF, online RL, and model‑based data improvement.
• Design the systems abstractions that connect research ideas to production‑scale RL runs: trainers, rollout workers, reward models, evaluators, data queues, experience buffers, and checkpoint promotion.
• Build evaluation and feedback loops for omni behavior: turn‑taking, interruption, timing, emotional response, audiovisual coherence, instruction following, and real‑time interaction quality.
• Optimize the end‑to‑end post‑training loop across rollout throughput, serving latency, GPU utilization, policy update efficiency, queueing, checkpoint overhead, and research iteration speed.
• Evolve the platform as algorithms, model architectures, reward definitions, data sources, and evaluation methods change.

• A PhD — completed, or in its final stretch — in ML, RL, or a related field, with research depth shown through publications, a strong lab/advisor, or substantial open-source work.
• Solid understanding of RL/post‑training methods: policy optimization, reward modeling, preference optimization, rejection sampling, KL control, evaluation, and data feedback loops.
• Ability to reason about model behavior and training dynamics: reward…