System Modeling (Dynamic Systems Simulation)
Originally posted April 3, 2026 by the employer. Relisted by the employer on November 14, 2026.
About the role
As a Member of Technical Staff in System Modeling (Dynamic Systems Simulation), you will build custom behavioral simulators that bridge the gap between SPICE-level physical reality and AI framework-level algorithmic abstraction. Your work enables the simulation of complex analog and mixed-signal systems without losing critical dynamics that govern system behavior.
What you'll do
- Develop high-performance PyTorch or JAX components that model complex, time-varying circuit-based dynamic systems.
- Implement simulation approaches that connect high-level neural network design down to fundamental differential equations governing system behavior.
- Balance computational efficiency with physical accuracy to simulate systems faster than traditional circuit simulators.
What you'll need
- MS/PhD in Electrical Engineering, Computer Engineering, Applied Physics with focus on solid-state devices or VLSI, or BS with equivalent depth in circuit simulation.
- Knowledge of analog and mixed-signal circuit design principles, including transistor-level concepts and modeling nonidealities like noise, mismatch, and process variations.
- Proficiency in PyTorch or JAX, specifically building custom autograd functions and integrating numerical solvers (such as Neural ODEs) to represent dynamic processes.
- Theoretical and practical grasp of linear and non-linear dynamics, state-space representations, and solving differential equations within a machine learning context.
- Experience modeling and mitigating noise using stochastic processes, including stochastic differential equations (SDEs) or Bayesian filtering.
- Proven industry experience building high-fidelity circuit simulations, alongside practical experience profiling, optimizing, and stabilizing non-trivial large-scale ML systems.
Nice to have
- Strong Python engineering skills including modular design, testing, packaging, and CI.
- Experience with PyTorch internals (autograd, custom modules, low-level ops, torch.compile or graph capture/compile flows).
- Experience with CUDA, Triton, or GPU programming approaches such as custom kernels, memory hierarchy analysis, and basic performance tuning.
- Familiarity with JAX, NumPy, TensorFlow, Modal, SciPy, or HPC patterns (MPI, NCCL, distributed training).
- Experience applying efficiency techniques such as quantization, sparsity, pruning, distillation, and kernel fusion.
Skills: system modeling, dynamic systems, simulation
More semiconductor jobs: SoC design jobs
First seen in our index 110 days ago, well above the 72-day median for SoC/Chip Architecture roles.
SoC/Chip Architecture · SOC Architecture
|
Open roles in category
520
|
Median days since first seen
72 d
|
Category median salary (all employers)
$228k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Unconventional AI | All employers we track in this specialty (520 roles · 70 employers) |
|---|---|---|
| Open roles in this specialty | 3 | 520 |
| Open roles in the wider Digital Design & Verification family | 7 | 3639 · 123 employers |
| Median days since first seen | 110 d | 72 d (−38 d vs this employer) |
| Median salary (USD postings) | n/a | $228k |
Skills observed across this category: system modeling, dynamic systems, simulation
Who's hiring in this category
- Qualcomm · 92 open roles · median 73 d
- NVIDIA · 69 open roles · median 86 d
- AMD · 41 open roles · median 72 d
- Apple · 40 open roles · median 14 d
- Arm Holdings · 28 open roles · median 17 d
- Micron Technology · 21 open roles · median 113 d
How we counted: 520 open SoC/Chip Architecture (SOC Architecture) roles from 70 employers tracked in the SemiconductorJobs index, counted 14 Nov 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there, and is counted in the wider Digital Design & Verification family row. Days are counted from when our index first saw each posting, not from the employer's original posting date.