Principal Machine Learning Engineer – Wafer Fabrication - Fremont, CA, United States
Originally posted 9 September 2026 by the employer — open 1 day.
About the role
This role focuses on embedding and scaling AI/ML solutions within the manufacturing ecosystem to drive product development, yield improvement, screening, and process optimization. The position involves broad exposure to Development, Engineering, Manufacturing, and Process Teams within semiconductor wafer fabrication.
What you'll do
- Develop and validate models for yield improvement, screening accuracy, and process optimization.
- Collaborate with Photonics designers and process, reliability, and manufacturing engineers to align ML approaches with product objectives.
- Deploy AI/ML within manufacturing systems using Edge AI, API serving, Containerization, and Cloud-based training and inference.
- Partner with industrial and MES software engineers to integrate AI/ML pipelines within existing production workflows.
- Develop reusable data pipelines, analytical tools, dashboards, and model-monitoring methods.
- Support design of experiments, process characterization, and continuous improvement activities.
What you'll need
- Minimum 5 years of experience with AI, including ML.
- Expertise in deep learning frameworks such as Pytorch, TensorFlow.
- Expertise in deep learning architectures such as CNNs, RNNs, GANs.
- Expertise in ML methods such as Random Forests and Gradient Boosting.
- Proficiency in ML model deployment through RESTful APIs, containerization, and container orchestration.
- Experience with SQL and modern data-processing or data-platform technologies.
- Bachelor’s degree in Electrical Engineering, Computer Science, Physics, or related field with specialization in Data Science, AI/ML, or Statistics.
Nice to have
- Programming skills in Python and experience with common data-science and machine-learning libraries.
- Familiarity with high-performance ML inference (CUDA, Libtorch, ONNX Runtime, C++ programming and data structures).
- Experience with cloud-based ML training and inference using AWS, GCP, Azure, or Databrix.
- Familiarity with machine vision such as defect detection and OCR.
- Familiarity with big data frameworks such as Hadoop and Spark.
- Exposure to manufacturing, wafer fabrication, photonics, telecommunications.
Skills: Machine Learning Engineer, wafer fabrication, yield improvement, deep learning frameworks, ML model deployment
This role has been open 1 day — well below the 63-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
508
|
Median days open
63 d
|
Median salary
$224k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Coherent Corp | All employers we track in this specialty (508 roles · 79 employers) |
|---|---|---|
| Open roles in this specialty | 2 | 508 |
| Open roles in the wider Software, Firmware & Systems family | 35 | 5650 · 142 employers |
| Median days open | 50 d | 63 d (−13 d vs this employer) |
| Median salary (USD postings) | — | $224k |
Skills observed across this category: Machine Learning Engineer, wafer fabrication, yield improvement, deep learning frameworks, ML model deployment
Who's hiring in this category
- Qualcomm · 107 open roles · median 98 d
- NVIDIA · 88 open roles · median 60 d
- AMD · 44 open roles · median 63 d
- Micron Technology · 27 open roles · median 47 d
- Mobileye · 21 open roles · median 44 d
- NXP Semiconductors · 11 open roles · median 64 d
How we counted: 508 open AI/ML Hardware Engineering (AI ML Hardware) roles from 79 employers tracked in the SemiconductorJobs index, counted 10 Sept 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.