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Mlir Engineer Jobs in Utah (NOW HIRING)

Mlir Engineer information

What is an MLIR engineer?

An MLIR Engineer is a software engineer who specializes in working with the Multi-Level Intermediate Representation (MLIR) compiler framework. MLIR is an open-source project designed to provide a flexible and extensible infrastructure for building domain-specific compilers and optimizing machine learning workloads. MLIR Engineers typically design, implement, and optimize compiler passes, dialects, and transformations to improve the performance and portability of machine learning models across different hardware architectures. They often collaborate with machine learning researchers, compiler engineers, and hardware teams to enable efficient model deployment.

What are the key skills and qualifications needed to thrive as an MLIR engineer, and why are they important?

To thrive as an MLIR Engineer, you need a solid background in compiler theory, C++ programming, and experience with machine learning frameworks or intermediate representations. Familiarity with LLVM, the MLIR ecosystem, and version control systems like Git is typically required, as well as a relevant degree in computer science or a related field. Strong problem-solving skills, attention to detail, and effective communication are valuable soft skills in this role. These qualifications and skills are crucial for building robust compiler infrastructure, collaborating across teams, and ensuring efficient support for diverse machine learning workloads.

What are some common challenges faced by MLIR engineers when integrating new dialects into existing compiler infrastructures?

MLIR Engineers often encounter challenges such as ensuring compatibility between new dialects and existing IR components, maintaining performance optimizations, and avoiding redundancy or conflicts in transformations. Integrating a new dialect requires deep understanding of both the MLIR ecosystem and the target hardware or frameworks, which can be complex due to rapid evolution in machine learning compilers. Close collaboration with backend and frontend teams is essential to address integration issues and to ensure that new dialects meet project requirements and performance goals.

What is the difference between Mlir Engineer vs Compiler Engineer?

AspectMlir EngineerCompiler Engineer
Required CredentialsBachelor's or Master's in Computer Science, experience with MLIR, compiler frameworksBachelor's or Master's in Computer Science, strong programming skills, compiler theory knowledge
Work EnvironmentResearch labs, tech companies focusing on AI/ML, compiler development teamsSoftware development firms, tech companies, open-source projects
Industry UsageAI/ML hardware optimization, compiler infrastructure, MLIR frameworkSoftware optimization, language development, system software

Both roles involve working with compiler technologies, but Mlir Engineers specialize in the MLIR framework and its applications in AI/ML hardware optimization, while Compiler Engineers focus broadly on compiler design and implementation across various software systems.

What are popular job titles related to Mlir Engineer jobs in Utah?

For Mlir Engineer jobs in Utah, the most frequently searched job titles are:

What job categories do people searching Mlir Engineer jobs in Utah look for?

The top searched job categories for Mlir Engineer jobs in Utah are:

Senior Deep Learning Tools Engineer - CUDA Tile

Nvidia

Salt Lake City, UT • On-site

$101K - $138K/yr

Full-time

Re-posted 2 days ago


Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies


Job description

NVIDIA is building advanced compiler technologies to accelerate AI workloads, and we are looking for an engineer focused on performance validation, analysis, and tracking. In this role, you will work at the intersection of deep learning compilers, GPU systems, and automation infrastructure, ensuring that performance improvements are measurable, scalable, and continuously validated over time. Do you want to help drive the performance of next-generation compilers.

Are you excited by how GPU performance powers breakthroughs in deep learning, autonomous systems, and high-performance computing. We are seeking a talented Deep Learning Compiler & Tools Engineer focused on CUDA Tile (Performance & Infrastructure) to join our team. You will collaborate closely with compiler developers, infrastructure providers, and hardware teams to build systems that track, analyze, and improve performance across rapidly evolving AI workloads.

If you're passionate about performance, systems, and building infrastructure that drives real-world impact, we want to hear from you. What You'll Be Doing: Design and develop performance testing frameworks for deep learning compilers and workloads Build and maintain automated pipelines (CI/CD) to continuously track performance across models, hardware, and compiler changes Implement benchmarking systems to measure latency, throughput, and efficiency of AI and HPC workloads Analyze performance trends over time and identify regressions, bottlenecks, and optimization opportunities Partner with compiler and architecture teams to debug and resolve performance issues Develop tools and dashboards for performance visualization, reporting, and insights Enable scalable testing across diverse GPU systems and environments Improve infrastructure to ensure reliable, reproducible, and high-signal performance data What We Need to See: BS, MS, or PhD (or equivalent experience) in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or related field 5+ years of software engineering experience, including experience in performance engineering, benchmarking, or systems optimization Strong programming skills in Python (C++ is a plus) Experience with CI/CD systems and automation frameworks Familiarity with hardware-aware performance analysis (GPUs, accelerators, or similar systems) Experience working with deep learning frameworks such as PyTorch, TensorFlow, JAX, or TensorRT Background in data analysis, profiling, and regression tracking Ability to debug complex system-level issues across software and hardware layers Ways to Stand Out from the Crowd:: Experience with GPU performance analysis and optimization Understanding of compiler internals (LLVM, MLIR, CUDA compilation flow) Experience building performance dashboards and large-scale telemetry systems Familiarity with hardware/software co-design or low-level performance tuning Experience with distributed testing infrastructure or large-scale benchmarking systems With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered one of the most desirable employers in the technology industry. Our teams are tackling some of the most challenging problems in AI, deep learning, and accelerated computing.

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 May 10, 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.


What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


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