Engineering Manager, Data Labeling Platform

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.

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  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.

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  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.

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  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.

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  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.

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  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

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  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.

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

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related technical field (or equivalent experience).

• 10+ overall years of professional software engineering experience, including 2+ years as a technical lead or engineering manager

• Hands-on Engineering: Strong background as a Software Engineer, Data Engineer, or AI Application Engineer, with excellent system design skills and deep hands-on expertise in Python.

• AI & Data Engineering: Proven experience in architecture-level understanding of data pipelines, distributed systems, and integrating machine learning models into production workflows (specifically auto-labeling or human-in-the-loop paradigms).

• Leadership Capability: Experience leading technical initiatives, mentoring engineers, or formally managing a team. We welcome senior individual contributors (ICs) with demonstrated tech-lead experience who are ready to make the leap to formal management.

• Exceptional Communication: Exceptional stakeholder management skills. You must be comfortable translating deeply technical constraints into strategic updates for VPs, while translating high-level business goals into operational directives for the Data Factory workforce.

• Experience with human-in-the-loop data programs - managing data labeling platforms for RLHF or preference data or red teaming, supporting test, vision or multi-modal datasets.

• Experience applying models to reduce or assist human effort in a labeling workflow, such as automated pre-labeling, model-based quality evaluation, or agent-driven internal tooling, with measurement to back it up.

• Working proficiency in TypeScript or JavaScript, and experience delivering applications where interaction design materially affects user throughput and error rate.

• Background in multimodal data: video, 3D and point cloud, speech, or document understanding.

This posting is for an existing vacancy. 

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