$29 - $49 USD hourly
About the role
This role involves developing, implementing, and optimizing machine learning models to support the design, analysis, and development of advanced acoustic and RF devices.
What you'll do
- Apply artificial neural network (ANN) and machine learning techniques to support microwave CAD and semiconductor design projects.
- Develop and evaluate ANN-based models for acoustic devices, electromagnetic (EM) analysis, and parametric modeling.
- Assist in the development of data-driven models for RF filter design and performance prediction.
- Support neural network-based multiphysics modeling of electrical, acoustic, thermal, and mechanical interactions.
- Build and optimize LLM-powered agents with capabilities such as tool integration, workflow automation, and multi-step reasoning.
- Research emerging developments in AI, machine learning, and data science, and explore their applications in semiconductor design and manufacturing optimization.
What you'll need
- Currently pursuing an M.S. or Ph.D. degree in Computer Science, Electrical Engineering, Applied Mathematics, Data Science, or a related technical field.
- Solid foundation in machine learning, deep learning, statistical modeling, and data analysis techniques.
- Proficiency in Python programming; experience with C++ and SQL is a plus.
- Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or Keras.
- Familiarity with data analysis and scientific computing libraries, including NumPy, Pandas, and Scikit-learn.
- Experience developing machine learning workflows, data pipelines, or scalable software solutions through coursework, research, or projects.
Nice to have
- Coursework or project experience in large language models (LLMs), generative AI, agent-based systems, or deep learning applications.
- Coursework or project experience in RF, microwave engineering, signal processing, computational electromagnetics, or semiconductor devices.
- Experience working with simulation-generated datasets and physics-based models: knowledge of Physics-Informed Neural Networks (PINNs), surrogate modeling, or digital twin methodologies.
- Familiarity with reinforcement learning, Bayesian optimization, or other optimization techniques for engineering applications.
- Familiarity with Git, Jira, and MLflow for version control and model management.
Skills: machine learning, neural network, acoustic devices, RF filter design, Python, PyTorch
More semiconductor jobs: AI chip design jobs · Irvine jobs
This role has been open 31 days — well below the 104-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
469
|
Median days open
104 d
|
Category median salary (all employers)
$238k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Skyworks | All employers we track in this specialty (469 roles · 73 employers) |
|---|---|---|
| Open roles in this specialty | 5 | 469 |
| Open roles in the wider Software, Firmware & Systems family | 57 | 6531 · 156 employers |
| Median days open | 57 d | 104 d (−47 d vs this employer) |
| Median salary (USD postings) | — | $238k |
Skills observed across this category: machine learning, neural network, acoustic devices, RF filter design, Python, PyTorch
Who's hiring in this category
- NVIDIA · 105 open roles · median 82 d
- Qualcomm · 91 open roles · median 129 d
- AMD · 29 open roles · median 122 d
- Mobileye · 15 open roles · median 96 d
- Analog Devices · 14 open roles · median 57 d
- Micron Technology · 14 open roles · median 69 d
How we counted: 469 open AI/ML Hardware Engineering (AI ML Hardware) roles from 73 employers tracked in the SemiconductorJobs index, counted 8 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 — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.