Originally posted 30 September 2026 by the employer.
This role focuses on DSP and embedded NPU performance across Snapdragon platforms.
About the role
This role involves architecting, analyzing, and optimizing DSP and embedded NPU performance across Snapdragon platforms, with a focus on architectural analysis, optimization, and deployment of machine learning software.
What you'll do
- Analyze, design, and optimize Machine learning kernels on ML HW accelerator for performance, power, and area efficiency.
- Conduct architectural analysis and benchmarking of ML subsystems, identifying bottlenecks and proposing solutions.
- Collaborate with hardware and software teams to define and implement enhancements in ML HW microarchitecture, memory hierarchy, and dataflow.
- Develop and validate performance models for AI workloads, including signal processing and ML inference, on embedded platforms.
- Prototype and evaluate new architectural features for ML HW, including quantization, compression, and hardware acceleration techniques.
- Support system-level integration, performance testing, and demo prototyping for commercialization of optimized ML solutions.
What you'll need
- Solid background in DSP architecture, embedded NPU design, and low-power AI systems.
- Proven experience in performance analysis, benchmarking, and optimization on any embedded processors (DSP, ARM, RISC-V, NPU).
- Strong programming skills in Embedded C/C++, Python.
- Experience with embedded platforms, real-time operating systems, and hardware/software co-design.
- Expertise in both fixed-point and floating-point implementation, with a focus on ML/AI workloads.
- Strong fundamentals of Power modeling and Power analysis.
Skills: ML HW accelerator, ML subsystems, ML HW microarchitecture, performance models for AI workloads, hardware acceleration techniques
This role has been open 2 days — well below the 73-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
510
|
Median days open
73 d
|
Median salary
$225k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Qualcomm | All employers we track in this specialty (510 roles · 76 employers) |
|---|---|---|
| Open roles in this specialty | 104 | 510 |
| Open roles in the wider Software, Firmware & Systems family | 691 | 5930 · 145 employers |
| Median days open | 106 d | 73 d (+33 d vs this employer) |
| Median salary (USD postings) | — | $225k |
Skills observed across this category: ML HW accelerator, ML subsystems, ML HW microarchitecture, performance models for AI workloads, hardware acceleration techniques
Who's hiring in this category
- Qualcomm (this employer) · 106 open roles · median 106 d
- NVIDIA · 84 open roles · median 72 d
- AMD · 42 open roles · median 66 d
- Micron Technology · 31 open roles · median 56 d
- Mobileye · 20 open roles · median 69 d
- NXP Semiconductors · 15 open roles · median 59 d
How we counted: 510 open AI/ML Hardware Engineering (AI ML Hardware) roles from 76 employers tracked in the SemiconductorJobs index, counted 2 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.