Candidate fit35%78
JD evidence
Physics-informed neural networks
Simulation
Model validation
Candidate proof
Documented ML project
Time-series portfolio evidence
Gaps
Experimental data
Apple · Herzliya
ML Research Engineer – Physics-Informed AI
Physics-informed ML role combining simulation, experimental data and model validation.
network first78opportunity score
Verified openChecked 7 Sept 2026
Hard-constraint gateVerify before applying
- work authorizationIsrael work authorization
Sponsorship is not stated on the official posting.
Responsibilities
- Train physics-informed models
- Integrate simulation and measurements
Requirements
- Physics-informed neural networks
- Simulation
- Model validation
- Experimental data
Compensation: not stated
The employer did not state compensation. No estimate is presented as official.
Explainable score · 2026-08-v1
Why this score?
78/100
Career upside20%86
JD evidence
Research-to-production scope
Candidate proof
Research-to-industry portfolio
Gaps
None material
Compensation10%70
JD evidence
Candidate proof
Gaps
Employer compensation not stated
Actionability20%72
JD evidence
Official source captured
Candidate proof
Targeted CV draft available
Gaps
Israel work authorization
Personal factors15%82
JD evidence
Candidate proof
English-working market preference
Gaps
None material
Source confidence 96%Explanation coverage 95%