Member of Technical Staff — RL Research (New PhD Grad)

Job description

## About Nuance Labs Nuance 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 more. Backed by Accel, Lightspeed, South Park Commons, and NVIDIA, we combine frontier research with ruthless engineering needed for consumer-grade, real-time systems. 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. That rethinking starts with full-duplex: an AI that listens and speaks simultaneously, perceives emotion in real time, and responds with a face that actually reflects it. It's an extremely hard problem, and we're developing foundation models designed for it from the ground up. ## 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 posting is aimed at researchers who are completing — or have recently completed — a PhD and want to do their best work at a fast-moving frontier lab. 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. You’ll help build Nuance’s RL/post-training stack from 0→1 and scale it from 1→10. That means turning rapidly evolving research ideas into reliable training systems: defining the abstractions, choosing or modifying frameworks, wiring together rollout workers and trainers, building reward/evaluation loops, debugging failure modes, and making the system fast enough for researchers to iterate. For Nuance, post-training is not limited to text. Our models are omni from the ground up: audio, video, language, and real-time full-duplex interaction. We need RL and post-training methods that improve interactive behavior, timing, interruption, emotional response, audiovisual coherence, and real-time conversational quality. This is a high-ownership role with direct impact on how Nuance models improve after pretraining — and a place to grow fast alongside people who’ve built these systems before. ## 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. ## About the Role Model quality is ultimately a data problem. The best architecture and the best training run can't outrun bad, slow, or poorly curated data — and at the scale we're operating, the difference between a good data pipeline and a great one shows up directly in the model. We're looking for someone who lives and breathes data at scale. You know how to build pipelines that are fast, reliable, and maintainable — and you're just as comfortable taking a researcher's messy processing script and turning it into something that runs on petabytes as you are designing a new pipeline architecture from scratch. Research moves fast here, and the ability to productionize quickly without losing fidelity is the core skill. Our data is multimodal — video, audio, and text — and the processing requirements are demanding: high throughput, low error rates, and strict quality filters. There's a lot of interesting engineering work here, and the impact is direct and measurable. ## What You'll Do - Design, build, and operate large-scale data pipelines for ingestion, processing, filtering, and curation of multimodal training data (video, audio, text) - Take research-grade data processing code and turn it into robust, production-level pipelines — quickly and without losing correctness - Optimize pipeline throughput and efficiency at scale; identify and eliminate bottlenecks across compute, I/O, and storage - Build and maintain data quality systems — deduplication, filtering, validation, and quality scoring at scale - Manage petabyte-scale datasets: storage architecture, versioning, lineage tracking, and cost efficiency - Work closely with researchers to understand data requirements and translate them into scalable processing systems - Build tooling and infrastructure that makes the research team faster — efficient data access, reproducible processing, and fast iteration loops ## What We're Looking For - Proven experience building and operating large-scale data pipelines in production — you've processed data at a scale where naive approaches break - Strong proficiency with distributed data processing frameworks — Spark, Ray, Dask, or similar — and a clear sense of when to use each - Solid software engineering fundamentals: you write clean, testable, maintainable code and understand why that matters when pipelines run unattended at scale - Experience with multimodal data (video, audio) is a strong plus — understanding of formats, codecs, and processing libraries (FFmpeg, decord, etc.) - Familiarity with ML data pipelines specifically — understanding of how data quality and format affect model training - Ability to move fast: you can take a prototype script from a researcher and ship a production version in days, not weeks ## Bonus Points - Experience building data pipelines for large-scale model training (pre-training or fine-tuning) - Familiarity with data versioning and lineage tools (DVC, Delta Lake, Apache Iceberg, etc.) - Experience with streaming data pipelines or online data processing - Prior work at an AI lab, video platform, or other data-intensive company - Contributions to open-source data tooling ## Compensation $200,000 – $300,000 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, etc.) from day one. - **AI-native tooling:** Do your best work with the best tools, including unlimited tokens. ## Benefits - **Health:** We offer a variety of plans that meet your needs, including an HDHP with ~$2,000 in annual HSA contributions by the company (roughly 2x what most big tech companies put in). - **Time off:** 15 days of PTO, 10 public holidays, and we close the office for a full week at year-end. - **Food:** Lunch, drinks, and snacks on us every workday. We observe boba tea Tuesdays and Thursdays. - **Commuter benefits:** Utilize pre-tax money (up to $340/month) for parking and transportation. - **401(k):**4% match (100% of 1st 3% + 50% of next 2% contributions). *Nuance Labs is an equal opportunity employer. We believe diverse teams build better AI.*