Efficient AI Systems, Principal Engineer (On-Device/Edge)
$200,800 - $301,200 USD yearly
Originally posted 30 April 2026 by the employer. Relisted by the employer on 7 August 2026.
Design and deploy deep learning models on the Qualcomm Ride platform and Snapdragon Ride AI accelerators.
About the role
As an AI Systems Team Lead and Principal Engineer, you will oversee a team of machine learning engineers designing and implementing Visual Language Action Models (VLAs), VLMs, and LLMs for low-level perception, end-to-end autonomous driving, and infotainment applications. Your technical focus centers on deploying deep networks to Snapdragon Ride AI accelerators on the Qualcomm Ride platform for L2/L3 ADAS and autonomous driving.
What you'll do
- Lead the design and implementation of Visual Language Action Models (VLAs), VLMs, and Vision Transformers (Cross Attention, Self-Attention) targeting accuracy-latency Pareto optimality. - Direct end-to-end autonomous driving network design, low-level perception systems, and deployment on the Qualcomm Ride platform. - Optimize deep neural networks for speed, accuracy, power consumption, and latency on Snapdragon Ride AI accelerators. - Develop methods for multi-modal fusion and lifting 2D features to BEV space across autonomous driving and infotainment applications. - Partition machine learning solutions at the SoC level and guide architectural decisions across systems-level software and hardware. - Engage with advanced R&D teams of automotive OEMs and collaborate across internal horizontal AI and stack teams.
What you'll need
- Bachelor's degree in Engineering, Computer Science, Information Systems, or a related field with 8+ years of Systems Engineering experience (or Master's with 7+ years, or PhD with 6+ years). - Strong knowledge of machine learning algorithms for automotive use-cases, including autonomous driving, vision, and lidar processing ML algorithms. - Experience with deep learning network design and deploying networks on resource-constrained devices. - Technical background in network quantization and efficient network development. - Willingness and ability to work in a laboratory environment with hazardous chemicals or machinery.
Nice to have
- PhD in a related field with 5+ years of relevant experience, Master's with 10+ years, or Bachelor's with 12+ years. - Previous experience leading machine learning engineers. - Hands-on experience with deep learning kernel/compiler optimization. - Track record of external publications or patents in machine learning.
Skills: machine learning algorithms, automotive use-cases, autonomous driving, vision, lidar processing ML algorithms, deep learning network design
This role has been open 198 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 | Qualcomm | All employers we track in this specialty (506 roles · 77 employers) |
|---|---|---|
| Open roles in this specialty | 104 | 506 |
| Open roles in the wider Software, Firmware & Systems family | 735 | 6250 · 144 employers |
| Median days open | 116 d | 87 d (+29 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: machine learning algorithms, automotive use-cases, autonomous driving, vision, lidar processing ML algorithms, deep learning network design
Who's hiring in this category
- Qualcomm (this employer) · 105 open roles · median 116 d
- NVIDIA · 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.