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Compiler Engineer - MLIR / PyTorch Infrastructure

Mythic AI · Posted 95 days ago

ic
3+ yrs expMLIRC++PythonCompiler DesignIR DesignCompiler PassesPyTorchTorch-MLIR

Architect Mythic's next-generation MLIR-based compiler stack that bridges analog AI accelerators with PyTorch, influencing the broader ML compiler ecosystem.

About us Mythic is building the future of AI computing with breakthrough analog technology that delivers 100× the performance of traditional digital systems at the same power and cost. This unlocks bigger, more capable models and faster, more responsive applications—whether in edge devices like drones, robotics, and sensors, or in cloud and data center environments. Our technology powers everything from large language models and CNNs to advanced signal processing, and is engineered to operate from –40 °C to +125 °C, making it ideal for industrial, automotive, aerospace, and defense.

We’ve raised over $100M from world-class investors including Softbank, Threshold Ventures, Lux Capital, and DCVC, and secured multi-million-dollar customer contracts across multiple markets.

About the role Join us in advancing the MLIR ecosystem at Mythic. You’ll help extend our existing high-level dialects and design a new hardware-aware low-level dialect, building conversion paths that bridge to our current IRs. Working closely with hardware engineers and ML developers, your work will expand interoperability with PyTorch and other frameworks, laying the groundwork for long-term innovation. The result: an MLIR-based stack that unifies hardware constraints with developer needs and accelerates adoption of modern ecosystems. 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)

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