Partcl is developing the next generation of chip design automation tools that are 1000x faster than legacy options using GPUs and AI, aiming to solve massive-scale problems in physical AI and level up hardware engineering.
Requirements
- Python + PyTorch skills
- Systems intuition
- Some CUDA exposure
- Tooling chops : you’ve at least dabbled in GPU profiling/perf analysis
- Any exposure to EDA / chip design flows
- Reinforcement Learning beyond toy notebooks
- Research or serious projects in compilers / programming languages / databases
Responsibilities
- Cook + train custom models to go after NP‑complete optimization problems (smart heuristics that actually ship, fr)
- Spin high‑perf CUDA kernels for training + inference and make them zoom
- Speedrun parsers that chew through massive files without choking
- Build ultra‑lean, cache‑friendly data structures — minimal mem, max throughput
- Hook up an LLM interface to those structures so users can literally talk to their design
Other
- Internship
- Will sponsor
- Pull up with your resume/portfolio/GitHub. Let’s build.
