Staff AI Application Engineer - Heterogeneous Computing
Axelera
SeniorAbout the position
Join a dynamic team to build the model enablement platform and enhance AI performance across various systems.
Tech stack
- python
- cpp
- pytorch
- onnx
- mlir
- cuda
Requirements
Required:
- 8+ years of engineering experience in systems, compilers, runtimes or performance, including system-level design ownership
- Hands-on experience with heterogeneous computing across CPU, GPU, DSP, NPU or FPGA, including reasoning about compute, memory bandwidth, latency and data movement
- Solid fundamentals in computer architecture, operating systems and parallel programming
- Familiarity with ML graph representations and compiler concepts: graph capture (torch.export, FX, ONNX), lowering, operator fusion, partitioning, memory planning
Nice to have:
- Hands-on experience with MLIR or another compiler framework (TVM, XLA, TorchInductor, IREE)
- Experience with an inference runtime or execution engine (ONNX Runtime execution providers, a custom NPU or DSP runtime)
- Experience with large-model execution techniques such as offloading, expert caching, speculative decoding, pipelining across devices
Responsibilities
- Build the platform that engineers and AI agents use to bring up models: reusable tools to partition, compile, run and profile
- Own how a PyTorch or ONNX model is captured, analysed and split across the AIPU's matrix and RISC-V vector units
- Extend that split to large models, cascades and multi-model pipelines, for example with expert offloading and caching across device and host memory
- With the application team and the runtime team, bring each model to its best end-to-end performance
Benefits
- Attractive compensation package, including a pension plan
- Extensive employee insurances and the option to get company shares
- An open culture that supports creativity and continual innovation
- Collaborative ownership and freedom with responsibility