Simulation Engineer

DensityAI•Mountain View, CA

4d•$200,000 - $360,000

About The Position

Build and own the software simulation stack for our AI accelerator — the functional, instruction-, and performance-level models that the compiler, runtime, and architecture teams develop and validate against long before silicon exists. Your simulators are how the rest of the company brings up software early, explores the design space, and predicts behavior; when they're fast and accurate, the whole program moves faster.

Requirements

Exceptional modern C++ for performance-critical systems software, plus strong Python for tooling and infrastructure
5+ years building simulators, emulators, virtual platforms, ISS, or large-scale systems/infrastructure software
Solid computer-architecture fundamentals — pipelines, memory hierarchy, ISAs, and accelerator/GPU-class datpaths — enough to model hardware faithfully
Real simulation/modeling judgment: functional vs. cycle-approximate trade-offs, validating against a reference, and optimizing the simulator's own performance
Strong software-engineering practice — testing, CI, profiling, and debugging large long-lived codebases

Nice To Haves

gem5 / QEMU / SystemC-TLM / Spike or custom simulators
JIT / dynamic binary translation
ML frameworks & workloads
compiler/runtime exposure
distributed simulation
HW/SW co-design

Responsibilities

Design, build, and maintain the software simulators and models of our AI accelerator — functional / ISA simulators, cycle-approximate performance models, and the C++/Python infrastructure around them
Build the pre-silicon development surface for the software teams: golden reference models, co-simulation, trace generation, and validation harnesses the compiler and runtime develop against daily
Keep the simulators fast enough to be the daily target — multithreading, JIT / binary translation, and distributed runs so real workloads finish in reasonable time
Correlate models against RTL, emulation, and first silicon as they arrive, and drive the deltas back into the model to keep it trustworthy
Use and develop AI-assisted tool flows to accelerate model development and validation