Machine Learning Operations Contractor - Fremont, CA, United States
Originally posted 3 June 2026 by the employer.
Deploy scalable machine learning models for semiconductor laser yield prediction and high-performance inference using LibTorch, CUDA, and ONNX.
About the role
The Machine Learning Operations Contractor develops and deploys AI/ML models within production to support semiconductor laser design and manufacturing. The role focuses on yield improvement, screening accuracy, and design optimization across wafer processes and die-level testing.
What you'll do
- Enhance data pipelines across wafer processes, die-level test, and experimentation. - Develop supervised and unsupervised models for yield prediction and performance screening. - Perform data pre-processing, feature extraction, dimensionality reduction, clustering, classification, and regression. - Validate models using standard techniques and deploy scalable solutions across cloud infrastructure and on-premises high-performance inference. - Integrate domain knowledge from semiconductor laser physics into machine learning models for improved interpretability. - Collaborate with photonics researchers and process engineers to align ML implementations with experimental and production objectives.
What you'll need
- B.S. with 5 years of industry experience or M.S./Ph.D. with 2 years of industry experience in Electrical Engineering, Computer Science, Physics, Photonics, or a related field. - Expertise with ML frameworks including TensorFlow, PyTorch, and scikit-learn. - Proficiency in Python and C/C++ programming data structures for performance optimization. - Experience in high-performance ML inference using LibTorch, CUDA, or ONNX. - Experience with cloud-based ML platforms (AWS, GCP, Azure) and MLOps platforms such as Kubernetes. - Proven track record in AI/ML deployment and integration within high-visibility projects.
Nice to have
- Experience with NN architectures such as CNNs, AEs, GANs, and ensemble learning methods including gradient boosting and random forests. - Familiarity with inverse design concepts. - Background in photonics, semiconductor devices, or manufacturing yield optimization.
Skills: data pipelines, supervised models, unsupervised models, yield prediction, NN architectures, TensorFlow
This role has been open 142 days — well above the 86-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
507
|
Median days open
86 d
|
Median salary
$236k
|
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 (507 roles · 76 employers) |
|---|---|---|
| Open roles in this specialty | 2 | 507 |
| Open roles in the wider Software, Firmware & Systems family | 34 | 6297 · 144 employers |
| Median days open | 94 d | 86 d (+8 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: data pipelines, supervised models, unsupervised models, yield prediction, NN architectures, TensorFlow
Who's hiring in this category
- Qualcomm · 105 open roles · median 115 d
- NVIDIA · 89 open roles · median 57 d
- AMD · 34 open roles · median 107 d
- Micron Technology · 26 open roles · median 58 d
- Mobileye · 20 open roles · median 84 d
- Analog Devices · 18 open roles · median 42 d
How we counted: 507 open AI/ML Hardware Engineering (AI ML Hardware) roles from 76 employers tracked in the SemiconductorJobs index, counted 24 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.