Senior Manager, Product - Perception Data

NVIDIA•Us, CA

About The Position

NVIDIA is developing AI and Accelerated Computing solutions for the Automotive industry. We are looking for a Senior Manager of Product to join our data team and lead a product group. Your core mission is to deeply understand what perception models need to improve — and translate that understanding into high-quality, high-value data annotation strategies that directly drive model performance gains. This role sits at the intersection of perception research, data engineering, and labeling operations. You will work closely with perception scientists to identify model performance gaps, define annotation requirements, own the data roadmap, and drive end-to-end delivery across labeling vendors and internal teams. The best candidate combines strong product instincts with genuine technical depth in ML data pipelines and perception tasks.

Requirements

BS degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).
8+ years of product management experience, with at least 2+ years in a role directly involving ML/AI model development, data pipelines, or training data strategy.
Genuine understanding of perception model fundamentals.
Strong cross-functional communication skills.
3+ years of experience leading or mentoring a team of product managers, tech leads, or equivalent.

Nice To Haves

Fluent in Mandarin Chinese a strong plus (team operates across US and China time zones).
Direct experience in autonomous driving, robotics, or ADAS product development
Familiarity with 3D reconstruction, SLAM, NeRF, LiDAR point cloud annotation, and multi-sensor fusion.
Exposure to model-in-the-loop, human-in-the-loop, auto labeling, or VLM labeling.

Responsibilities

Develop a deep understanding of how training data impacts perception model performance across tasks including 3D object detection, lane/road structure recognition, traffic sign and traffic light detection, and semantic segmentation.
Partner with perception researchers and engineers to translate model capability gaps into concrete data requirements.
Own annotation ontology design: define labeling taxonomies, attribute schemas, edge case handling rules, and inter-annotator consistency standards for perception network tasks such as 3D bounding boxes, lane elements, traffic sign/light classification and association.
Anticipate how changes in data density, ROI size, and label complexity affect labeling throughput and delivery capacity; provide data-backed forecasts to stakeholders.
Collaborate with global engineering and labeling teams to ensure data quality and explore how DNN/LLM/VLM improves the performance of data labeling.