Join Mythic to advance the MLIR ecosystem, extending high-level dialects and designing a new hardware-aware low-level dialect.
Posted by employer 11 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Compiler Engineer focuses on enhancing the MLIR ecosystem by developing high-level dialects and creating a new low-level dialect that aligns with hardware specifications. Proficiency in MLIR, PyTorch, and collaboration with hardware engineers and machine learning developers are essential for success. This position is well-suited for experienced engineers with a strong background in compiler design and an understanding of hardware-software integration. Mythic's innovative approach to AI computing positions the team at the forefront of technology in various demanding industries.
From the original posting
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.