$140,800 - $211,200 USD yearly
Originally posted 28 October 2026 by the employer.
About the role
As a Machine Learning Research Engineer, Senior at Qualcomm AI Research, you will conduct applied and fundamental deep learning research across software, hardware, and systems. The role focuses on model efficiency, training methods, and platform research to optimize machine learning performance across power, memory, and computation.
What you'll do
- Drive systems innovations for model efficiency on device and in the cloud, including model compression, model sparsity, and quantization optimization methods.
- Conduct fundamental machine learning research to develop new models and training methods across large language models, deep generative models, reasoning models, and agentic frameworks.
- Perform platform research to enable compute paradigms such as compute in memory, on-device training, edge-cloud federated learning, and quantum machine learning.
- Design, develop, and test software for machine learning frameworks that optimize models to run on edge devices.
- Research, design, develop, and implement components of machine learning compilers for HW Accelerators.
- Design, implement, and train DL and RL algorithms using high-level languages and frameworks.
What you'll need
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field with 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related experience; or Master's degree with 1+ year; or PhD.
- Strong background in computer science and deep learning basics.
- Strong programming skills in Python and PyTorch.
- Experience designing and implementing complex training and evaluation pipelines.
- Analytical, development, and debugging skills.
Nice to have
- Experience with LLM, LVM, and LMM models and neural network architectures.
- Experience with machine learning accelerators, optimizing algorithms for hardware acceleration cores, and working with heterogeneous or parallel computing systems.
- Experience designing and developing generalized AI solutions, including retrieval and agentic systems.
- Experience in LLM reasoning or inference acceleration research.
- Experience with on-device AI model production or mobile and edge device environments.
- First-author publication record at conferences such as NeurIPS, ICML, or ICLR.
Skills: deep learning, PyTorch, quantization, model compression, HW Accelerators
More semiconductor jobs: Qualcomm jobs · AI chip design jobs · San Diego jobs
First seen in our index 15 days ago, well below the 108-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
479
|
Median days since first seen
108 d
|
Category median salary (all employers)
$238k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Qualcomm | All employers we track in this specialty (479 roles · 73 employers) |
|---|---|---|
| Open roles in this specialty | 103 | 479 |
| Open roles in the wider Software, Firmware & Systems family | 757 | 6715 · 161 employers |
| Median days since first seen | 122 d | 108 d (−14 d vs this employer) |
| Median salary (USD postings) | n/a | $238k |
Skills observed across this category: deep learning, PyTorch, quantization, model compression, HW Accelerators
Who's hiring in this category
- NVIDIA · 109 open roles · median 87 d
- Qualcomm (this employer) · 100 open roles · median 125 d
- AMD · 21 open roles · median 127 d
- Mobileye · 15 open roles · median 101 d
- Analog Devices · 14 open roles · median 50 d
- Micron Technology · 14 open roles · median 65 d
How we counted: 479 open AI/ML Hardware Engineering (AI ML Hardware) roles from 73 employers tracked in the SemiconductorJobs index, counted 13 Nov 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there, and is counted in the wider Software, Firmware & Systems family row. Days are counted from when our index first saw each posting, not from the employer's original posting date.