Hiring: Member of Technical Staff — RL Research (Experienced) Company: Nuance Labs Location: Seattle, WA Job Posted Time: 2026-09-09 18:04:43 Target Skills & Keywords : Emotion, React, Spark About the job Required Skills: •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 experienced researchers and engineers who’ve operated at a senior to senior-staff level at big tech or a leading research lab. Everyone at Nuance is MTS — we don’t run title ladders — but we’re hiring people who have already done this work at scale. •This role is broader than a traditional RL algorithm role. You will be expected to understand modern post-training methods and build the infrastructure needed to run them at scale. The work spans RL method development, rollout generation, reward modeling, policy optimization, evaluation, data feedback loops, serving, observability, and distributed execution. •You will 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. •What You’ll Own •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 Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!