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Mlir Jobs (NOW HIRING)

Mythic

Compiler Engineer - MLIR / PyTorch Infrastructure

$110K - $144K/yr

The Compiler Engineer will advance the MLIR ecosystem by extending existing high-level dialects and designing a new hardware-aware low-level dialect, working closely with hardware engineers and ML ...

Mythic

Compiler Engineer - MLIR / PyTorch Infrastructure

$110K - $144K/yr

The role involves advancing the MLIR ecosystem by architecting compiler flows and enhancing interoperability with PyTorch and other frameworks. Responsibilities : • Architect the migration of the ...

DensityAI

Compiler Engineer -- MLIR

Mountain View, CA · On-site

Responsibilities : • Own MLIR dialect design and lowering passes for our AI accelerator -- defining the high-level tensor IR, async / streaming semantics, and sharded-tensor types that bridge ML ...

Mythic

Compiler Engineer - MLIR / PyTorch Infrastructure

$110K - $144K/yr

The Compiler Engineer will advance the MLIR ecosystem by architecting the migration of the existing compiler flow into MLIR and building conversion paths, working closely with hardware engineers and ...

DensityAI

Compiler Engineer -- MLIR

Mountain View, CA · On-site

$200K - $360K/yr

About the role Own MLIR dialect design and lowering passes for our AI accelerator -- defining the high-level tensor IR, async / streaming semantics, and sharded-tensor types that bridge ML frameworks ...

DensityAI

Compiler Engineer - MLIR

Mountain View, CA · On-site

About the role Own MLIR dialect design and lowering passes for our AI accelerator - defining the high-level tensor IR, async / streaming semantics, and sharded-tensor types that bridge ML frameworks ...

DensityAI

Compiler Engineer - MLIR

Mountain View, CA · On-site

$200K - $360K/yr

About the role Own MLIR dialect design and lowering passes for our AI accelerator - defining the high-level tensor IR, async / streaming semantics, and sharded-tensor types that bridge ML frameworks ...

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Mlir information

What is MLIR and what is it used for?

MLIR (Multi-Level Intermediate Representation) is an open-source compiler infrastructure project developed by the LLVM community. It provides a flexible and extensible intermediate representation framework, which is used to build reusable and modular compiler components for a wide range of domains, such as machine learning, hardware acceleration, and domain-specific languages. MLIR enables developers to create custom dialects and transformations, making it easier to optimize and target various hardware architectures. Its primary goal is to facilitate the development of high-performance and portable compilers.

What are the key skills and qualifications needed to thrive as an MLIR (Multi-Level Intermediate Representation) developer, and why are they important?

To thrive as an MLIR developer, you need a strong background in compiler theory, C++ programming, and familiarity with LLVM infrastructure, typically supported by a degree in computer science or a related field. Experience with tools such as the MLIR framework, LLVM, and related build systems like CMake is highly valuable. Analytical thinking, problem-solving, and effective collaboration are important soft skills for innovating and working within open-source or cross-functional teams. These skills ensure the efficient design and optimization of compiler components, driving advancements in machine learning and hardware support.

What is the difference between Mlir vs Machine Learning Engineer?

AspectMlirMachine Learning Engineer
Required CredentialsTechnical knowledge of compiler infrastructure, programming skills in C++/PythonDegree in Computer Science, Data Science, or related fields; experience with ML frameworks
Work EnvironmentResearch and development in compiler and software infrastructure teamsDeveloping, testing, and deploying machine learning models in various industries
Employer & Industry UsageTech companies, AI research labs, compiler development firmsTech companies, startups, AI-focused organizations
Common Search & Comparison IntentUnderstanding technical roles in compiler infrastructureLearning about careers in machine learning and AI

While Mlir focuses on compiler infrastructure and software development for optimizing machine learning models, Machine Learning Engineers primarily design and implement ML models for practical applications. Both roles require technical expertise, but Mlir is more specialized in compiler technology, whereas Machine Learning Engineers work directly on AI solutions.

How does an engineer working with MLIR typically collaborate with different teams in a product development environment?

Engineers specializing in MLIR (Multi-Level Intermediate Representation) often work closely with compiler teams, hardware architects, and machine learning researchers to optimize and integrate new features. Collaboration frequently involves participating in design discussions, code reviews, and cross-functional meetings to align on performance goals and implementation strategies. These engineers also contribute to open-source projects and may mentor junior team members or coordinate with external contributors. Working in such a dynamic and interdisciplinary environment helps ensure that MLIR tools remain robust, efficient, and aligned with evolving hardware and ML frameworks.
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Infographic showing various Mlir job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 75% In-person, and 25% Remote job distribution.
Compiler Engineer - MLIR / PyTorch Infrastructure

Compiler Engineer - MLIR / PyTorch Infrastructure

Mythic

Austin, TX • On-site

$106K - $139K/yr

Full-time

Posted 27 days ago

Job description

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)
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.
At Mythic, we foster a collaborative and respectful environment where people can do their best work. We hire smart, capable individuals, provide the tools and support they need, and trust them to deliver. Our team brings a wide range of experiences and perspectives, which we see as a strength in solving hard problems together. We value professionalism, creativity, and integrity, and strive to make Mythic a place where every employee feels they belong and can contribute meaningfully.
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