--- title: 'Member of Technical Staff — ML Infra (Data) at Nuance Labs' canonical: 'https://feeny.ai/job/member-of-technical-staff-ml-infra-data-nuance-labs-seattle-efnr92fwhx14' type: 'job' last_seen: '2026-09-11' --- # Member of Technical Staff — ML Infra (Data) at Nuance Labs - **Company:** Nuance Labs - **Location:** Seattle, WA - **Posted:** 2026-06-05 - **Last confirmed live:** 2026-09-11 - **Apply:** https://job-boards.greenhouse.io/nuancelabs/jobs/4277601009 ## 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 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. ## About Nuance Labs ## Company Overview - **One-liner**: Nuance Labs is building a real-time, full-duplex audiovisual AI model that can see, hear, reason, speak, and express emotion simultaneously, enabling face-to-face interaction with machines. - **Entity Type**: Private (Seed stage; raised $10M in a seed round led by Accel) - **Headquarters**: Seattle, Washington, United States - **Founded**: Not publicly disclosed (likely 2024 or 2025 based on funding date) - **Founders**: Fangchang Ma (CEO), Edward Zhang (CTO), Karren Yang (Chief Scientist) – all PhDs from MIT with prior experience at Apple ## Core Business - Primary industry: Artificial Intelligence / Software Development - Target customers: B2B (enterprise and consumer product integration) and potentially B2C (direct-to-consumer AI interfaces) - Mission or purpose statement: “We are creating a world where people can finally talk to every product, face to face.” – building a “human foundation model” with social and emotional intelligence. ## Products & Services - **The Full-Duplex Engine**: A real-time audiovisual model that perceives, reasons, and responds within 500ms – handling interjections, nodding, backchanneling, tone, and expression. The system is designed to cross the uncanny valley with warm, engaging personality. - **Human Foundation Model**: An AI that learns human behavior by predicting the next audio and visual token (similar to how transformers predict the next word). It reads tone, expression, hesitation, and responds in real time. ## Market Standing - **Valuation**: Not disclosed (private company) - **Key Metric**: Total funding – $10M in a seed round closed on October 9, 2025 - **Notable Investors/Partners**: Lead investor Accel (with 3 total investors in the seed round) - **Growth Signals**: - Small team of ~10 employees with 9.1% monthly headcount growth - Strong hiring momentum: 6+ active job postings for research and engineering roles - Team includes PhDs from MIT, UW, Oxford, CMU, Johns Hopkins, and alumni from Apple, Meta, Amazon AGI, and Discord - High LinkedIn follower growth (+4.5% monthly) ## Competitive Advantages - **Full-duplex real-time capability**: 500ms latency floor – a hard requirement for natural conversation that most current AI systems cannot achieve. - **Multimodal emotional intelligence**: Reads tone, expression, and hesitation, not just text. - **Elite founding team**: PhDs from top institutions with deep experience shipping ultra-low-latency ML products at Apple and other tech giants. - **First-mover in “human foundation model”**: Applying next-token prediction to audio-visual human behavior, not just language. ## Strategic Focus - Current priorities: Advance the Full-Duplex Engine, scale the model, and build the infrastructure for real-time audiovisual interaction. The company is hiring across pretraining infrastructure, RL research, model optimization, speech synthesis, video diffusion, and MLLM training. - Long-term direction: Enable face-to-face AI interaction for every product, moving computing interfaces closer to human communication. ## Why Work Here - **Culture**: “Small, fast-moving research team” with an “exceedingly high bar” – only the very best talent. Every member has massive ownership, deep trust, and the opportunity to shape both the technology and the company from the ground up. - **Location**: Based in Seattle, WA. The company appears to be primarily in-office (all listed roles are in Seattle, and the team is small). - **Notable perks**: Not explicitly mentioned, but the emphasis on research autonomy, high-impact work, and a founding team from top AI labs suggests a strong engineering culture with cutting-edge projects. - **Open roles** (as of mid-2026): Research Scientist (Speech Synthesis, Video Diffusion, MLLM Training), Machine Learning Research Engineer, ML Infra Engineer, Systems Engineer (Real-Time Engine), AI Product Engineer, and more. ## Sources 1. [nuancelabs.ai](https://www.nuancelabs.ai/) 2. [nuancelabs.ai/careers](https://www.nuancelabs.ai/careers) 3. [nuancelabs.ai/about](https://www.nuancelabs.ai/about) 4. [job-boards.greenhouse.io/nuancelabs](https://job-boards.greenhouse.io/nuancelabs) 5. 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