Internship Dpo Jobs (NOW HIRING)

Job Summary:
Nuance Labs is a pioneering company focused on building photorealistic, real-time AI avatars with emotional intelligence. They are seeking a deeply technical Member of Technical Staff to lead reinforcement learning and post-training for large-scale omni models, requiring a PhD graduate who can develop and scale their RL/post-training stack.
Responsibilities:
• 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.
Qualifications:
Required:
• A PhD — completed, or in its final stretch — in ML, RL, or a related field, with research depth shown through publications, a strong lab/advisor, or substantial open-source work.
• Solid 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.
• Exposure to RL/post-training pipelines through research, internships, or open-source — with frameworks such as verl, ms-swift, OpenRLHF, or equivalent, and familiarity with rollout serving systems such as vLLM. You don’t need to have run these at production scale yet; you need to learn fast and go deep.
• Strong software engineering fundamentals and the appetite to build real systems, not just prototypes.
• Curiosity and adaptability toward new RL algorithms, model architectures, serving systems, evaluation methods, and research ideas.
Preferred:
• Hands-on experience with omni or multimodal post-training for audio-video-language models, especially long-context or real-time interactive systems.
• Experience with PPO, GRPO, DPO, online RL, RLHF/RLAIF, reward modeling, preference data, synthetic data generation, or model-based data improvement.
• Prior 0→1 experience building post-training systems, RL pipelines, agent training systems, evaluation platforms, or model improvement loops.
• 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.
Company:
Nuance Labs an AI research company is developing the first human foundation model that understands and displays emotion in real time. Founded in 2024, the company is headquartered in Seattle, USA, with a team of 11-50 employees. The company is currently Early Stage.