Engineering Manager, Data Labeling Platform

NVIDIA•Santa Clara, CA

1d•$200,000 - $391,000

About The Position

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 a 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.