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Machine Learning Compiler Engineer Jobs in California

Nvidia

Senior Compiler Engineer - DL

Santa Clara, CA · On-site

$143K - $189K/yr

NVIDIA is hiring software engineers for its Deep Learning Compiler (DLC) team. Academic and commercial groups around the world are using GPUs to power a revolution in deep learning, enabling ...

Datapelago

Sr. Compiler Engineer

Mountain View, CA · On-site

$122K - $168K/yr

Senior Compiler Engineer Mountain View, CA About DataPelago: DataPelago is at the forefront of ... machine learning, and cloud-native computing. We are looking for specialists to join our ...

Datapelago

Sr. Compiler Engineer

Mountain View, CA · On-site

$122K - $168K/yr

Senior Compiler Engineer Mountain View, CA About DataPelago: DataPelago is at the forefront of ... machine learning, andcloud-native computing. We are looking for specialists to join our engineering ...

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All JobsMachine Learning Compiler Engineer JobsMachine Learning Compiler Engineer Jobs in California

Machine Learning Compiler Engineer information

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$31.1K

$127.1K

$191K

How much do machine learning compiler engineer jobs pay per year?

As of Jul 9, 2026, the average yearly pay for machine learning compiler engineer in California is $127,083.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,200.00 and $153,000.00 per year, depending on experience, location, and employer.

What is a Machine Learning Compiler Engineer job?

A Machine Learning Compiler Engineer focuses on optimizing and building compilers that translate high-level machine learning models into efficient code runnable on specialized hardware (e.g., GPUs, TPUs). They work on improving performance, memory usage, and execution efficiency of ML workloads by designing compiler optimizations, code generation techniques, and leveraging frameworks like LLVM or MLIR. Their role bridges the gap between ML researchers and hardware engineers, ensuring models run efficiently on target platforms.

What are the typical daily responsibilities of a Machine Learning Compiler Engineer?

As a Machine Learning Compiler Engineer, your daily responsibilities often include designing and implementing new compiler optimizations, collaborating with machine learning researchers to support model deployment, and debugging performance or correctness issues in compiled code. You may participate in code reviews, write technical documentation, and conduct benchmarking to evaluate how machine learning models perform on various hardware backends. Close collaboration with hardware engineers, software architects, and data scientists is common, ensuring end-to-end solutions meet both research and production requirements. Staying updated with the latest advancements in both compiler technology and machine learning frameworks is also a key aspect of the role.

What are the key skills and qualifications needed to thrive in the Machine Learning Compiler Engineer position, and why are they important?

A Machine Learning Compiler Engineer needs a deep understanding of computer science fundamentals, compiler theory, and experience with machine learning frameworks, often supported by a relevant degree in computer science or engineering. Proficiency with tools such as LLVM, TVM, MLIR, and languages like C++, Python, and CUDA is typically required, and familiarity with hardware architectures is a plus. Strong problem-solving, teamwork, and communication skills are essential for collaborating with cross-functional teams and addressing complex system issues. These capabilities are important for designing and optimizing compilers that enable scalable and efficient deployment of machine learning models on diverse hardware platforms.

What are the most commonly searched types of Machine Learning Compiler Engineer jobs in California? The most popular types of Machine Learning Compiler Engineer jobs in California are:
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Machine Learning Compiler Engineer jobs near you
Senior Deep Learning Compiler Engineer - XLA

Senior Deep Learning Compiler Engineer - XLA

NVIDIA

Santa Clara, CA • On-site

$122K - $168K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Summary:
NVIDIA is a leader in AI computing and is seeking versatile software engineers for their XLA team. The role involves developing compiler optimization algorithms for deep learning workloads and collaborating with various teams to enhance the performance of next-generation AI systems.
Responsibilities:
• 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.
Qualifications:
Required:
• 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.
• Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team.
Preferred:
• 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.
• A history of mentoring junior engineers and interns is a bonus.
• 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.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.
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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