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 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.

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

• 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.

Bonus Points

• 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.

Compensation

$250,000 – $350,000 base salary, plus meaningful equity. We think long-term ownership matters and structure equity accordingly.

Logistics

• Location: In-person in Seattle, five days a week — we believe in the compounding value of working shoulder-to-shoulder.
• Visa sponsorship: We sponsor visas (O-1, H-1B, green card) from day one.
• AI-native tooling: Do your best work with the best tools, including unlimited tokens.

Benefits

• Health: HSA plan with ~$2,000 in annual company contributions — roughly 2x what most big tech companies put in.
• Time off: 15 days of PTO plus public holidays, and we close the office for a full week at year-end.
• Food: Lunch, drinks, and snacks on us every workday — the small thing that quietly makes the day better.
• Commuter benefits: We help cover the cost of getting to the office.
• 401(k): In the works.

Nuance Labs is an equal opportunity employer. We believe diverse teams build better AI.