Compiler Engineer – MLIR / PyTorch Infrastructure
Job description
About us
About the role
Here's what you will do
- Architect the migration of the existing compiler flow into MLIR, defining dialects, passes, and lowering strategies.
- Build conversion paths between MLIR and Mythic’s custom low-level IR to keep both flows operational during migration.
- Define validation infrastructure within MLIR, including interpretation or execution paths for simulation and debugging.
- Enable compilation by extending MLIR integration across analog accelerators and digital subsystems.
- Leverage Torch-MLIR where PyTorch inputs are available, and guide future integration with PyTorch 2.0 compiler technologies (TorchInductor, TorchDynamo, Torch-MLIR)
Here's the background we hope you will have
- 3+ years of experience in compiler or high-performance systems development.
- Proficiency in modern C++ (C++14/17/20) and Python.
- Direct, hands-on experience with MLIR, including dialect design, compiler passes, or lowering pipelines.
- Strong understanding of compiler IRs and transformations, with the ability to reason about lowering from high-level ops to hardware-aware representations.
The following would be nice to have, but is not required
- Experience architecting complete MLIR flows: from frontend dialects down to hardware-aware dialects, including conversion to and from existing IRs.
- Familiarity with PyTorch compiler technologies, especially Torch-MLIR and integration paths with PyTorch 2.0 (TorchDynamo, TorchInductor).
- Knowledge of dataflow architectures, scheduling, and memory orchestration.
- Background in heterogeneous or specialized accelerators (e.g., analog compute, NPUs, GPUs, DSPs).
What we offer
- Shape the MLIR and PyTorch integration strategy for novel AI hardware.
- Opportunity to contribute upstream and influence the broader ML compiler ecosystem.
- Collaborate with a team spanning hardware, runtime, and compiler design.
- Competitive compensation, equity, and comprehensive benefits.
Originally posted on Himalayas
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