Nuance Labs

Technology

MemberofTechnicalStaffMLDataInfra

$200–300k Seattle, Washington, United States
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Member of Technical Staff — ML Data Infra at Nuance Labs. Skills: Data pipelines, ML data pipelines, Multimodal data. Design large-scale data pipelines. Build large-scale data pipelines”

Industry & Context.

Technology
Problems you'll solve

Identify and eliminate bottlenecks

What They're Looking For.

Must Have

Proven experience building and operating large-scale data pipelines in production, Proficiency with distributed data processing frameworks, Solid software engineering fundamentals

Nice to Have

Experience with multimodal data, Familiarity with ML data pipelines, Experience building data pipelines for large-scale model training, Familiarity with data versioning and lineage tools, Experience with streaming data pipelines, Prior work at an AI lab, Prior work at a video platform, Prior work at a data-intensive company, Contributions to open-source data tooling

What You'll Do.

Design large-scale data pipelines

Build large-scale data pipelines

Operate large-scale data pipelines

Ingest multimodal training data

Process multimodal training data

Filter multimodal training data

Curate multimodal training data

Turn research code into production pipelines

Optimize pipeline throughput

Optimize pipeline efficiency

Eliminate bottlenecks

Build data quality systems

Maintain data quality systems

Manage petabyte-scale datasets

Track dataset lineage

Track dataset versioning

Manage dataset cost efficiency

Translate data requirements into systems

Build tooling for research team

Build infrastructure for research team

How You'll Work.

Team & Collaboration

Work closely with researchers

Full 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 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. 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 e

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