Member of Technical Staff — RL Research

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.