Nuancelabs

Member of Technical Staff — RL Research (Experienced)

Seattle, Washington · Staff+

H1B sponsorship available$300k-$500kDetected 41 days ago

SwiftReactAlgorithmsMachine LearningLogisticsHRISFirewallResearchAdaptability

About the role

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.
This role is broader than a traditional RL algorithm role.

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
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.
You will be expected to understand modern post-training methods and build the infrastructure needed to run them at scale.
You will build Nuance's RL/post-training stack from 0→1 and scale it from 1→10.
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.
We believe diverse teams build better AI.

Significant hands-on experience with RL, RLHF, RLAIF, post-training, alignment, or large-scale fine-tuning for modern foundation models.
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.
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.

$300,000 - $500,000 base salary, plus meaningful equity. We think long-term ownership matters and structure equity accordingly.
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.

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.
Commuter benefits: We help cover the cost of getting to the office.

About Nuance Labs
Labs is building photorealistic, real-time AI avatars with emotional intelligence:
a full-duplex audiovisual system that can listen, speak, react, interrupt, and respond like a real person.
We're a research company, with PhDs from MIT, UW, Oxford, CMU, and Johns Hopkins, and industry experience from Apple, Meta, Amazon AGI, and Discord.
The team is small, the work is real, and the problems are unsolved.
How Nuance Differentiates
Most conversational AI avatars today are hacks - a face slapped on a speech-to-speech pipeline, stuck in the uncanny valley: emotionless, mechanical, one-turn-at-a-time.
Current systems take 2-5 seconds to respond; natural conversation requires sub-500ms.
That's a 10x improvement, and it demands rethinking the entire stack.
We're looking for a deeply technical Member of Technical Staff to own RL and post-training for large-scale omni models.

This listing is sourced directly from Nuancelabs's careers page and normalized into a canonical job model.