Senior MLOps Engineer
$184,000–$287,500 year
On-siteSanta Clara, California, United States
Job Summary
Design and operate end-to-end data and ML pipelines that ingest, validate, process, and transform multimodal sensor data from camera, lidar, and radar into training, evaluation, and validation datasets. Own architecture, implementation, and operations for cloud pipelines supporting NVIDIA's autonomous driving technology from levels L2 through L4. Build observable MLOps systems for model training, ground truth generation, and continuous evaluation at AV scale, translating customer requirements into production systems with perception, ML, data labeling, infrastructure, and product teams. Set technical direction, roadmaps, metrics, and operational benchmarks while delivering against program milestones. Contribute through design reviews, implementation, debugging, code reviews, and mentorship across Python, C++, distributed systems, cloud infrastructure, CI/CD, and data platforms.
Required Qualifications
- Bachelor's or equivalent experience, Master's, or PhD in Computer Science, Electrical Engineering, or a closely related field (or equivalent experience)
- 8+ years of engineering experience designing and delivering production distributed systems
- Technical leadership as a senior individual contributor delivering large-scale systems
- Experience with MLOps, data pipelines, and cloud distributed systems
- Proficiency in Python and C++ for system-level and performance-critical implementation
- Experience operating end-to-end data or ML pipelines for reliability, scale, and observability
- Prior experience in one or more of the following domains: Autonomous Vehicles, Robotics, Computer Vision, Deep Learning, or GPU-accelerated computing
- Communication skills that align collaborators and drive execution across functions
- A record of ownership, accountability, and customer-focused engineering
Desired Qualifications
- Experience with AV data platforms handling petabyte-scale sensor data
- Hands-on contributions to production MLOps or data infrastructure
- Experience with automotive or robotic systems, including real-world sensor data pipelines
- Background in distributed cloud systems, workflow orchestration, and large-scale CI/CD
- Familiarity with 3D geometry, perception pipelines, or data generation based on simulated environments
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