System Modeling (Computation)
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 (Computation), you will build simulation frameworks that evaluate and iterate across all layers of physics-based computing systems for machine learning workloads.
What you'll do
- Develop physics-based surrogate models of device- and system-level behavior in unconventional compute with clean, composable abstractions.
- Enable cross-layer optimization and algorithm-hardware tradeoff exploration via end-to-end autodiff.
- Work closely with hardware and algorithm teams to support high-level algorithm development and low-level verification of novel, analog hardware.
What you'll need
- MS or PhD in a quantitative field (AI/ML, Computer Science, Physics, Electrical Engineering, Applied Math), or a BS with equivalent research and engineering depth.
- Expertise in numerical differential equation solvers (e.g., ODE, SDE, DDE) and their implementation on parallel architectures (e.g., Rosenbrock methods, Euler-Maruyama, adjoint methods, implicit solvers).
- Experience with high-performance, customized GPU kernel development for numerical methods, including GPU memory optimization and multi-GPU scaling.
- Experience building neural network surrogate models (e.g., Neural ODEs) for complex dynamical systems.
- Understanding of modern AI/ML architectures and training/inference workflows.
- Experience implementing and debugging ML models in PyTorch or similar, with practical experience profiling, optimizing, and stabilizing non-trivial large-scale ML systems.
Nice to have
- Python engineering skills with a focus on API design, strong typing, and composability, plus experience building open-source scientific libraries or simulation frameworks from scratch.
- Experience with compiler-friendly ML paradigms and internals (e.g., JAX vmap/pmap/jit, PyTorch autograd/torch.compile, custom XLA or Triton kernels).
- Familiarity with analog dynamic systems, transient responses, and nonidealities such as nonlinearity, quantization, random noise, and feedback/stability.
- Ability to reason across algorithm, software, compiler, runtime, and hardware layers, connecting model architecture choices to system performance metrics.
- Experience applying efficiency techniques natively within modeling frameworks (quantization, sparsity, pruning, distillation, kernel fusion).
Skills: system modeling, computation, software engineering, technical staff
More semiconductor jobs: Embedded software jobs
First seen in our index 110 days ago, well above the 65-day median for Systems Software Engineering roles.
Systems Software Engineering · Systems Software
|
Open roles in category
712
|
Median days since first seen
65 d
|
Category median salary (all employers)
$220k
|
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 (712 roles · 75 employers) |
|---|---|---|
| Open roles in this specialty | 1 | 712 |
| Open roles in the wider Software, Firmware & Systems family | 6 | 6731 · 166 employers |
| Median days since first seen | 110 d | 65 d (−45 d vs this employer) |
| Median salary (USD postings) | n/a | $220k |
Skills observed across this category: system modeling, computation, software engineering, technical staff
Who's hiring in this category
- Qualcomm · 175 open roles · median 73 d
- NVIDIA · 172 open roles · median 59 d
- AMD · 60 open roles · median 56 d
- Arm Holdings · 33 open roles · median 17 d
- Mobileye · 25 open roles · median 99 d
- Intel Corporation · 16 open roles · median 24 d
How we counted: 712 open Systems Software Engineering (Systems Software) roles from 75 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 Software, Firmware & Systems family row. Days are counted from when our index first saw each posting, not from the employer's original posting date.