About the job
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.
Responsibilities
Maintain a high technical bar by guiding robust software design, ensuring clean data engineering practices, and occasionally jumping into hands-on Python programming when solving complex architectural bottlenecks.
Build, mentor, and lead a high-performing team of software, data, and AI application engineers while fostering a culture of technical excellence, accountability, and continuous growth.
Serve as a critical bridge and strategic partner, aligning engineering roadmaps with high-level VPs, Research Leaders, and our Data Factory operations workforce.
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.
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.
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.
Optimize the platform for maximum scalability, data integrity, and throughput, ensuring the interface between human annotators and machine learning systems is seamless.
Qualifications
Minimum
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.
Strong background as a Software Engineer, Data Engineer, or AI Application Engineer, with excellent system design skills and deep hands-on expertise in Python.
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).
Experience leading technical initiatives, mentoring engineers, or formally managing a team.
Exceptional stakeholder management skills with the ability to translate deeply technical constraints into strategic updates for VPs and high-level business goals into operational directives for the Data Factory workforce.
Preferred
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.