AI Research Scientist, Scientific ML - sg, Singapore
Originally posted 18 September 2026 by the employer — open 0 days.
About the role
This role involves designing how physics constraints interact with neural network training for product development problems.
What you'll do
- Originate and advance PINNs methodology, designing physics-constrained loss function architectures.
- Validate digital twin ML components against domain physics and deliver validated prototypes.
- Lead research into Bayesian deep learning, active learning acquisition function design, ensemble uncertainty methods, and Bayesian experimental design frameworks.
- Own causal inference framework product development for reliability root cause analysis, including structural causal model (SCM) design, causal discovery, and causal intervention planning.
- Design physics-constrained generative model approaches for synthetic data generation.
- Demonstrate strong research output through preprints or patent disclosures.
What you'll need
- Master's or PhD in Artificial Intelligence, Machine Learning, Physics, Applied Mathematics, or a related field.
- 1–3 years work or research experience in scientific ML or applied AI roles for Master's degree, or open for PhD.
- Expert-level implementation capability in PyTorch or JAX.
- Experience with PINNs methodology design, physics-constrained loss function architecture, and digital twin modeling.
- Experience with Bayesian deep learning, Bayesian experimental design, active learning acquisition function design, and ensemble uncertainty quantification OR Structural causal models (SCM), causal discovery, and causal inference.
- Demonstrated research output (publication, preprint, PhD thesis chapter, or significant open-source scientific ML contribution).
- Ability to produce validated research prototypes with complete technical documentation.
Nice to have
- Diffusion models (DDPM, conditional diffusion).
- Graph neural networks (GNN).
- Neural ODEs.
- Foundation model fine-tuning.
- RL for scientific discovery.
- Top-venue publication (NeurIPS / ICML / ICLR / Nature MI).
- Materials science, semiconductor, or precision product development domain background.
Skills: PINNs, physics-constrained loss function, digital twin ML, PyTorch, JAX, Bayesian deep learning
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: PINNs, physics-constrained loss function, digital twin ML, PyTorch, JAX, Bayesian deep learning
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.