# Member of Technical Staff — RL Research

**Company:** [Nuance Labs](https://hotfix.jobs/companies/nuance-labs)
**Location:** Seattle, WA
**Role:** ML Engineering
**Salary:** $300k – $400k/yr
**Experience:** 7+ years
**Skills:** Rl, RLHF, Rlaif, Ppo, Dpo, Grpo, vLLM, Verl, Openrlhf, Ms-Swift, Reward Modeling, Policy Optimization, Preference Optimization, Large-Scale Training, Gpu Clusters
**Posted:** 2026-06-05

> Own RL and post-training infrastructure for omni foundation models. Build and scale rollout, reward, and policy systems from 0→1 for real-time audiovisual AI.

## Job Description

## 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 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.

## What We’re Looking For

- Hands-on experience with RL, RLHF, RLAIF, post-training, alignment, or large-scale fine-tuning for modern foundation models.
- Strong 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 hacking, unstable rewards, distribution shift, stale policies, mode collapse, over-optimization, noisy preferences, and evaluation mismatch.
- Practical experience building or operating RL/post-training pipelines with frameworks such as verl, ms-swift, OpenRLHF, or equivalent internal systems, including integration with rollout serving systems such as vLLM.
- 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.

## Bonus Points

- Prior 0→1 experience building post-training systems, RL pipelines, agent training systems, evaluation platforms, or large-scale model improvement loops.
- Experience with PPO, GRPO, DPO, online RL, RLHF/RLAIF, reward modeling, preference data, synthetic data generation, or model-based data improvement.
- Experience with omni or multimodal post-training for audio-video-language models, especially long-context or real-time interactive systems.
- Experience scaling mixed training/inference workloads across large GPU clusters.
- Experience with adjacent areas such as distributed pretraining, data infrastructure, inference serving, simulation, human/AI feedback collection, or evaluation infrastructure.
- Publications or substantial open-source contributions in RL, post-training, alignment, evaluation, ML systems, or model behavior.

## Compensation & Benefits

- $300,000 – $400,000 base salary, plus meaningful equity.
- HSA plan with ~$2,000 in company contributions.
- 15 days PTO + public holidays, and full week closure over the holidays.
- Lunch, beverages, and snacks provided daily.
- Commuter benefits.
- 401K in progress.

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