Senior Technologist - Machine Learning - sg, Singapore
Originally posted 18 September 2026 by the employer — open 0 days.
Apply Physics-Informed AI in product development.
About the role
This role involves making physics-informed AI work in product development as a deployed system that drives engineering decisions. The focus is on systematic deployment of AI at the physics level in precision product development.
What you'll do
- Design and own CNN, U-Net, and ViT systems for precision inspection, measurement, and defect classification.
- Validate and deploy PINNs methodology, owning product development implementation and physics-constraint validation.
- Architect active learning pipelines and Bayesian experimental design frameworks, integrating acquisition functions.
- Own the full ML platform including MLflow, Docker, AWS EKS/Kubernetes, LLM Gateway, CI/CD, observability, and model monitoring.
- Provide technical direction to the team, lead design and code reviews, and define data interface contracts.
What you'll need
- Bachelor's or Master's degree in Artificial Intelligence, Machine Learning, Computer Science, or related field.
- Minimum 3-5 years of hands-on technical experience.
- Demonstrated technical ownership of AI systems from design to product development deployment.
- Proven direction of a small ML or AI engineering team.
- Expert-level proficiency in Python.
- Expertise in PyTorch for custom loss functions and full training loop ownership.
- Experience with Computer Vision: CNN, U-Net, ViT for inspection, defect detection, and measurement.
- Experience with real-time anomaly detection from sensor and time-series product development data streams.
- Experience with Surrogate Modeling for data-efficient ML in limited-data scientific regimes.
- Experience with Active Learning & Bayesian Experimental Design, including acquisition function pipelines.
- Experience with Product Development MLOps, including MLflow, Docker, AWS EKS, and Observability Platforms like LangFuse or PortKey.
Nice to have
- Experience with PINNs, including validating ARS-originated designs and owning physics-constrained product development models.
- Experience with Reinforcement Learning.
- Experience with RAG / GraphRAG.
- Experience with LangFuse / PortKey.
- Experience with RLHF & reward modeling.
- Experience with AWS.
- Experience with LLM Finetuning (LoRA, QLoRA).
- Experience with LLM Distillation.
Skills: Deep Learning Systems Architecture, CNN, U-Net, ViT, MLOps Platform Ownership, 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: Deep Learning Systems Architecture, CNN, U-Net, ViT, MLOps Platform Ownership, 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.