Senior Manager, Machine Learning Ops Engineering - Automotive - CA, Santa Clara, United States
$272,000 - $431,250 USD yearly
Originally posted 10 September 2026 by the employer. Relisted by the employer on 11 September 2026.
About the role
NVIDIA is seeking a Senior MLOps Engineering Manager to join our Autonomous Driving organization in Santa Clara, CA to lead the build, development, and operation of large-scale, end-to-end data and ML pipelines. You will lead an engineering team building and operating cloud-scale pipelines that ingest, validate, process, and transform multimodal sensor data—including camera, lidar, and radar—into training, evaluation, and validation datasets.
What you'll do
- Lead and grow an MLOps engineering group managing end-to-end data pipelines supporting autonomous driving technology from levels L2 through L4. - Own the architecture, execution, and operational excellence of large-scale, cloud-native pipelines for multimodal sensor data ingestion, processing, labeling, and validation. - Drive the development of robust, scalable, and observable MLOps systems supporting model training, ground truth generation, and continuous evaluation at AV scale. - Partner with perception, ML, data labeling, infrastructure, and product teams to translate customer and program requirements into reliable production systems. - Define technical vision, roadmap, success metrics, and operational benchmarks while mentoring senior engineers and managers. - Operate across multiple layers of the stack, including Python, C++, distributed systems, cloud infrastructure, CI/CD, and data platforms.
What you'll need
- Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or a closely related field (or equivalent experience). - 10+ overall years of engineering experience, including crafting and coordinating production-grade distributed systems. - 5+ years of engineering management experience leading teams delivering large-scale systems. - Strong background in MLOps, data pipelines, and cloud-based distributed systems. - Proficiency in Python and C++, with the ability to guide system-level and performance-critical build decisions.
Skills: MLOps, data pipelines, autonomous driving, multimodal sensor data, cloud-native
This role has been open 200 days — well above the 87-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
506
|
Median days open
87 d
|
Median salary
$236k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | NVIDIA | All employers we track in this specialty (506 roles · 77 employers) |
|---|---|---|
| Open roles in this specialty | 88 | 506 |
| Open roles in the wider Software, Firmware & Systems family | 981 | 6250 · 144 employers |
| Median days open | 58 d | 87 d (−29 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: MLOps, data pipelines, autonomous driving, multimodal sensor data, cloud-native
Who's hiring in this category
- Qualcomm · 105 open roles · median 116 d
- NVIDIA (this employer) · 89 open roles · median 58 d
- AMD · 33 open roles · median 107 d
- Micron Technology · 26 open roles · median 59 d
- Mobileye · 20 open roles · median 85 d
- Analog Devices · 18 open roles · median 43 d
How we counted: 506 open AI/ML Hardware Engineering (AI ML Hardware) roles from 77 employers tracked in the SemiconductorJobs index, counted 25 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.