Engineering Manager, Data Labeling Platform - nvidia

Polluxa, Inc.; Santa Clara (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

NVIDIA is seeking an exceptional Engineering Manager to lead the Data Labeling Platform. You will oversee a team delivering end-to-end data annotation workflows, scalable backends, and AI-driven labeling solutions across multiple research areas.

The role emphasizes leading engineers, architecting scalable data systems, and delivering reliable annotation interfaces to support autonomous labeling and high-throughput operations.

Qualifications

• Experience leading software engineering teams
• Strong understanding of scalable systems and data platforms
• Proficiency in Python and data engineering concepts

Responsibilities

• Guide robust software design with a high technical bar
• Mentor and lead a high-performing team of software, data, and AI engineers
• Bridge engineering roadmaps with senior leaders and operations stakeholders
• Architect and drive auto-labeling/annotation applications leveraging multi-modal models
• Own data engineering layer: event logging, ETL, data lake metrics and dashboards
• Direct front-end engineering for annotation interfaces across modalities
• Scale platform for maximum throughput and reliability

Skills

Team leadership

System design

Python programming

Frontend development

Data pipelines

Job description

NVIDIA is looking for an exceptional Engineering Manager to lead, scale, and innovate our core Data Labeling Platform. This is a highly visible, high-impact role where you will bridge the gap between bleeding-edge AI engineering, scalable software systems, and massive-scale operations.

In this role, you will lead a team of highly talented engineers to design and build next-generation data annotation platform. The team develops and manages software that supports a high volume of active annotation projects across diverse research areas, like Nemotron, Cosmos, Robotics and Red teaming delivering a large quantity of annotations through NVIDIA's internal data operations and external annotation partners. Your team’s focus will be driving operational efficiency through annotation interfaces, scalable backend workflows, models-in-the-loop, auto-labeling systems, data pipelines, and intelligent orchestration tools. We are looking for a leader who can thrive across a wide spectrum of experience. Whether you are a seasoned Engineering Manager looking to take on an expanded scope, an entry-level Manager looking to solidify your leadership footprint, or a Principal/Staff Engineer (IC) with deep architectural roots ready to transition into people management, we want to hear from you.

What you'll be doing:

System Design & Programming: Maintain a high technical bar. You will remain close to the code, guiding robust software design, ensuring clean data engineering practices, and occasionally jumping into hands-on Python programming when solving complex architectural bottlenecks.

People Leadership: Build, mentor, and lead a high-performing team of software, data, and AI application engineers. Foster a culture of technical excellence, accountability, and continuous growth.

Stakeholder Management: Serve as a critical bridge and strategic partner, aligning engineering roadmaps with high-level VPs, Research Leaders, and our Data Factory operations workforce .

AI Application Engineering: Architect and drive the implementation of next-generation auto-labeling applications that leverage multi-modal models-in-the-loop to dramatically reduce human labeling latency.

Data Engineering & Analytics: Own the data engineering layer that makes annotation work measurable: event logging, ETL into NVIDIA's data lake, the metrics, dashboards, and alerting built on it against defined reliability and latency targets

Front-End & Annotation Interfaces: Direct front-end engineering for custom annotation interfaces across text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate.

Operations Scaling: Optimize the platform for maximum scalability, data integrity, and throughput, ensuring the interface between human annotators and machine learning systems is seamless.