Engineering Manager, Data Labeling Platform

NVIDIA is seeking an Engineering Manager to lead and scale its core Data Labeling Platform. The role is responsible for leading engineering teams, designing scalable annotation systems, developing AI-assisted labeling applications, managing data engineering and analytics, and optimizing platform operations.

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

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
• 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