Member of Technical Staff — ML Data Infra

Nuance Labs•Seattle, WA

3d•$200,000 - $300,000•Onsite

About The Position

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 Series A company ($60M raised) backed by Lightspeed, Accel, South Park Commons, NVentures, and Define Ventures, 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. 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. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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