# Member of Technical Staff — RL Research (Experienced) at Nuance Labs

- Company: Nuance Labs
- What the company does: We are building visual conversational AI that feels human. Backed by Accel, Lightspeed and South Park Commons.
- Company website: https://www.nuancelabs.ai/
- Type: Startups (AI role)
- Level: Senior
- Location: Seattle, Washington
- Work setup: On-site
- Pay: $300K to $500K base salary per year (USD)
- Posted: 2026-06-05
- Apply by: 2026-10-08
- Apply: https://job-boards.greenhouse.io/nuancelabs/jobs/4277561009
- Page: https://www.1752.vc/careers/jobs/nuance-labs-member-of-technical-staff-rl-research-experienced/

## 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 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.

## What they're looking for

- Significant hands-on experience with RL, RLHF, RLAIF, post-training, alignment, or large-scale fine-tuning for modern foundation models
- Deep understanding of RL/post-training methods: policy optimization, reward modeling, preference optimization, rejection sampling, KL control, evaluation, and data feedback loops
- A track record reasoning about model behavior and training dynamics: reward hacking, unstable rewards, distribution shift, stale policies, mode collapse, over-optimization, noisy preferences, and evaluation mismatch
- Experience with large-scale training or inference systems, including rollout generation, model serving, batching, queueing, GPU utilization, checkpointing, and debugging
- Understanding of omni post-training for real-time audio-video-language interaction: temporal alignment, interruption, emotional response, and multimodal evaluation
- Strong software engineering fundamentals, curiosity, and adaptability to new RL algorithms, model architectures, serving systems, evaluation methods, and research ideas

Tags: Research