NVIDIA
Engineering Manager, Data Labeling Platform
$200K–$391K
US, CA, Santa Clara +1 · Manager · Full-time · Posted Oct 7, 2026
- Location
- US, CA, Santa Clara
US, Remote - Experience
- 10+ overall years of professional software engineering experience, including 2+ years as a technical lead or engineering manager; senior individual contributors with demonstrated tech-lead experience may transition to formal management.
- Education
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related technical field (or equivalent experience).
About the job
Leads a team building and scaling NVIDIA’s data labeling platform, including annotation interfaces, data workflows, and model-assisted labeling applications.
What you'll do
- Lead, mentor, and develop software, data, and AI application engineers while maintaining a high technical bar.
- Architect annotation and auto-labeling systems, data pipelines, analytics, and custom interfaces across multiple modalities.
- Align engineering priorities with research leaders, executives, and data operations, and scale platform efficiency and reliability.
What they ask for
- 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 software, data, or AI application engineering background, with system design skills and deep hands-on expertise in Python.
- Experience with data pipelines, distributed systems, and integrating machine learning models into production workflows; experience leading technical initiatives or teams.
Nice to have
- Experience with human-in-the-loop data programs, labeling platforms, RLHF, preference data, or red teaming.
- Working proficiency in TypeScript or JavaScript; experience with multimodal data or model-assisted labeling workflows.
Pay
200,000 USD - 322,000 USD (base salary, Level 3); 248,000 USD - 391,000 USD (base salary, Level 4)
Tools
Python, TypeScript, JavaScript
AI focus
Builds auto-labeling applications using multimodal models-in-the-loop to reduce human labeling effort and latency; integrates machine learning models into production workflows.
Model-written summary · the original posting is authoritative