Member of Technical Staff — RL Research (New PhD Grad)
About the role
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 at scale. The 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.
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.