Nuance Labs
Technology
MemberofTechnicalStaff—MLDataInfra
Neural analysis suggests this role is
optimal for Mid+ candidates.
“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.
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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