Staff Engineer - Machine Learning - sg, Singapore
Originally posted 18 September 2026 by the employer — open 0 days.
Solve hard scientific problems in precision product development using physics-informed AI.
About the role
This role involves solving hard scientific problems in precision product development as part of a focused AI team. You will contribute to product development ML systems, including those based on physics-informed AI and Bayesian methods.
What you'll do
- Own MLflow experiment logging for assigned team model runs.
- Conduct model evaluations using standard metrics and produce structured evaluation reports.
- Validate training datasets, including feature distribution checks, label verification, and anomaly flagging.
- Build and train CNN-based models for image classification and defect detection.
- Package models in Docker and contribute to CI/CD scripts.
- Run inference tests and support deployment validation in product development environments.
What you'll need
- Bachelor's or Master's degree in AI, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
- Fresh graduate to 1 year of experience; demonstrated ML project competency is primary criterion.
- Strong proficiency in Python, including NumPy/Pandas basics.
- Foundational knowledge of PyTorch to build, train, and evaluate a basic neural network.
- Understanding of CNN Architecture Basics, including implementing a basic image classifier and conceptual understanding of convolutional layers.
- Familiarity with Surrogate Modeling Concepts and Active Learning Awareness.
- Basic knowledge of MLflow to log experiments, parameters, and metrics.
- Basic Docker skills to write a Dockerfile for a Python/ML application.
- Ability to perform Model Evaluation using standard metrics and produce structured evaluation reports.
Nice to have
- U-Net or ViT exposure.
- Uncertainty quantification basics, such as Monte Carlo dropout or ensemble methods.
- Introduction to Bayesian methods.
- Experience with time-series or sensor data.
- RL introduction.
- RAG basics or any LLM project experience.
- AWS fundamentals or entry-level cloud ML deployment experience.
Skills: MLflow, CNN-based models, defect detection, PyTorch, Docker, physics-informed AI
This role has been open 0 days — well below the 65-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
512
|
Median days open
65 d
|
Median salary
$226k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Western Digital | All employers we track in this specialty (512 roles · 77 employers) |
|---|---|---|
| Open roles in this specialty | 5 | 512 |
| Open roles in the wider Software, Firmware & Systems family | 44 | 5856 · 143 employers |
| Median days open | 0 d | 65 d (−65 d vs this employer) |
| Median salary (USD postings) | — | $226k |
Skills observed across this category: MLflow, CNN-based models, defect detection, PyTorch, Docker, physics-informed AI
Who's hiring in this category
- Qualcomm · 105 open roles · median 99 d
- NVIDIA · 85 open roles · median 64 d
- AMD · 42 open roles · median 65 d
- Micron Technology · 32 open roles · median 36 d
- Mobileye · 25 open roles · median 50 d
- Analog Devices · 14 open roles · median 21 d
How we counted: 512 open AI/ML Hardware Engineering (AI ML Hardware) roles from 77 employers tracked in the SemiconductorJobs index, counted 18 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.