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Mlir Jobs (NOW HIRING)

Tenstorrent

Software Engineer, AI Compiler

Austin, TX · On-site

$100K - $500K/yr

In this role you will lead development on TT-Forge, our MLIR-based compiler, and manage a team focused on scaling graph transformations, lowering passes, and kernel-level optimizations. You'll help ...

Nvidia

OR · On-site

$104K - $143K/yr

Code-generation for NVIDIA GPU backends using open-source compilers such as MLIR, LLVM and OpenAI Triton. * Designing user facing features in JAX and related libraries and other general software ...

Lemurian Labs

Front End Compiler

Santa Clara, CA

This is where the journey begins -- you'll build the systems that parse, validate, and lower representations from frameworks like PyTorch, StableHLO, ONNX, and MLIR dialects into our internal ...

Nvidia

Senior LLVM Compiler Engineer

Austin, TX

$103K - $142K/yr

In this role, you will work directly with LLVM, Clang, MLIR, and related opensource projects to upstream compiler functionality that currently lives in NVIDIA's downstream repositories. Your ...

Nvidia

OR · On-site

$104K - $143K/yr

In this role, you will work directly with LLVM, Clang, MLIR, and related opensource projects to upstream compiler functionality that currently lives in NVIDIA's downstream repositories. Your ...

Netflix

Product Machine Learning Research Leader

MLIR engages with the external research community spanning ML/AI, statistics, economics, and operations research through conferences, academic collaborations, and publications. At the same time, it ...

Neurophos Inc

Staff Compiler Engineer

Austin, TX · On-site

$250K - $315K/yr

MLIR experience is a plus * Experience writing programs that parse, analyze, and mutate programs as abstract syntax trees * Experience in instrumenting and debugging parallel programs * Experience ...

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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 June 2026, with employment types broken down into 99% Full Time, and 1% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.
Senior Deep Learning Compiler Engineer - XLA

Senior Deep Learning Compiler Engineer - XLA

Nvidia

Redmond, WA • On-site

$117K - $160K/yr

Full-time

Posted 14 days ago

Job description

NVIDIA's invention of the GPU 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the next era of computing - with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as "the AI computing company".

We are looking for versatile software engineers for our XLA team. NVIDIA is at the center for the AI revolution that's transforming how people live, work, and interact with technology. Come join us to build high-performance, production-grade software that's at the core of next-generation AI systems.

What you will be doing:

In this role, develop compiler optimization algorithms for deep learning workloads. You will optimize inference and training performance for the JAX framework and the OpenXLA compiler on NVIDIA GPUs at scale. You'll collaborate with our partners in deep learning framework teams and our hardware architecture teams to accelerate the next generation of deep learning software. The scope of these efforts include:

  • Crafting and implementing compiler optimization techniques for deep learning network graphs.

  • Designing novel graph partitioning and tensor sharding techniques for distributed training and inference.

  • Performance tuning and analysis.

  • Code-generation for NVIDIA GPU backends using open-source compilers such as MLIR, LLVM and OpenAI Triton.

  • Designing user facing features in JAX and related libraries and other general software engineering work.

  • Working closely with GPU hardware engineering teams to design AI compiler software features for next-generation GPUs.

What we need to see:

  • Bachelors, Masters or Ph.D. in Computer Science, Computer Engineering, related field (or equivalent experience).

  • 4+ years of relevant work or research experience in performance analysis and compiler optimizations.

  • Ability to work independently, define project goals and scope, and lead your own development effort adopting clean software engineering and testing practices.

  • Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design.

  • Strong foundation in architecture of CPU, GPUs or other high performance hardware accelerators. Knowledge of high-performance computing and distributed programming.

  • CUDA or OpenCL programming experience is desired but not required.

  • Experience with the following technologies is a huge plus: XLA, TVM, MLIR, LLVM, OpenAI Triton, deep learning models and algorithms, and deep learning framework design.

  • Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team. A history of mentoring junior engineers and interns is a bonus.

Ways to stand out from the crowd:

  • Experience working deep learning frameworks such as JAX, PyTorch or TensorFlow.

  • Extensive experience with CUDA or with GPUs in general.

  • Experience with open-source compilers such as XLA, LLVM, MLIR or TVM.

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March 1, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.#deeplearning
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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