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Machine Learning Compiler Engineer Jobs in California

We are seeking an AI Compiler Engineer with deep expertise in compiler technologies to join our ... The ideal candidate brings broad experience across machine learning, including reinforcement ...

Sr. Compiler Engineer

Mountain View, CA

$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 ...

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 ...

About the role As a Senior Principal Compiler Engineer you'll lead critical areas of our compiler ... Familiarity with machine learning models and frameworks. * Familiarity with accelerated computing.

New

This can involve anything from digging through PyTorch and machine learning models to determining how to map operations on to our underlying hardware. Responsibilities * Lead compiler engineering ...

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: Qualcomm is seeking a highly skilled Staff Engineer to drive development of LLVM-Ripple , its compiler toolchain ...

Showing results 41-60

Machine Learning Compiler Engineer information

See California salary details

$31.1K

$127.1K

$191K

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

As of Aug 20, 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?

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 as a machine learning compiler engineer?

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:

What are popular job titles related to Machine Learning Compiler Engineer jobs in California?

For Machine Learning Compiler Engineer jobs in California, the most frequently searched job titles are:

Infographic showing various Machine Learning Compiler Engineer job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $127,083 per year, or $61.1 per hour.

Sr. Machine Learning - Compiler Engineer III, AWS Neuron, Annapurna Labs

Amazon

Cupertino, CA

$128K - $177K/yr

Full-time

Re-posted yesterday


Amazon rating

7.4

Company rating: 7.4 out of 10

Based on 7,098 frontline employees who took The Breakroom Quiz

6th of 39 rated national retailers


Job description

Do you want to be part of AI revolution. At AWS our vision is to make deep learning pervasive for everyday developers and to democratize access to cutting-edge infrastructure. In order to deliver on that vision, we've created innovative software and hardware solutions that make it possible.

AWS Neuron is the SDK that optimizes the performance of complex ML models executed on AWS Inferentia and Trainium, our custom chips designed to accelerate deep-learning workloads
This role is for a senior software engineer in the Compiler team for AWS Neuron. As part of this role, you will be responsible for building next generation Neuron compiler which transforms ML models written in ML frameworks (e.g, PyTorch, TensorFlow, and JAX) to be deployed AWS Inferentia and Trainium based servers in the Amazon cloud. You will be responsible for solving hard compiler optimization problems to achieve optimum performance for variety of ML model families including massive scale large language models like Llama, Deepseek, and beyond as well as stable diffusion, vision transformers and multi-model models

You will be required to understand how these models work inside-out to make informed decisions on how to best coax the compiler to generate optimal implementation instruction. You will leverage your technical communications skill to partner with other teams and will be involved in pre-silicon design, bringing new products/features to market, and many other exciting projects. Experience in object-oriented languages like C++/Java is a must, experience with compilers or building ML models using ML frameworks on accelerators (e.g., GPUs) is preferred but not required

Experience with technologies like OpenXLA, StableHLO, MLIR will be added bonus!
Explore the product and our history. https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/neuron-cc/index.html
https://aws.amazon.com/machine-learning/neuron/
https://github.com/aws/aws-neuron-sdk
https://www.amazon.science/how-silicon-innovation-became-the-secret-sauce-behind-awss-success
AWS Utility Computing (UC) provides product innovations - from foundational services such as Amazon's Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS's services and features apart in the industry. As a member of the UC organization, you'll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services.
Key job responsibilities
You will design, implement, test, deploy and maintain innovative software solutions to transform Neuron compiler's performance, stability and user-interface

You will work side by side with chip architects, runtime/OS engineers, scientists and ML Apps teams to seamlessly deploy cutting edge ML models from our customers on AWS accelerators with optimal cost/performance benefits. You will have opportunity to become front-face of Neuron Compiler to work with open-source communities (e.g., StableHLO, OpenXLA, MLIR) and influence industry wide partners to pioneer optimizing cutting-edge ML workloads on AWS software and hardware. You will also work on building innovative features that will deliver best possible experiences for our customers - developers across the globe


A day in the life
As you design and code solutions to help our team drive efficiencies in compiler architecture, you'll create compiler optimization and verification passes, build features surface features and peculiarities of AWS accelerators to developers, implement tools to analyze numerical errors, and resolve the root cause of compiler defects. You'll also participate in design discussions, code review, and communicate with internal (other Neuron SDK and Amazon wide teams) and external stakeholders (open-source communities and respond to Neuron compiler related questions in open forums, e.g. GitHub)

Lastly, work in a startup-like development environment, where you're always working on the most important stuff.
About the team
About the Team
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews

We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying


About AWS
Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences

Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud


Mentorship & Career Growth
We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.


What Amazon employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


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About Amazon

Sourced by ZipRecruiter

Amazon.com, Inc., commonly known as Amazon, is an American multinational technology company. It was founded by Jeff Bezos in 1994 and initially started as an online marketplace for books. Since then, Amazon has expanded its operations and become one of the largest e-commerce companies in the world. Amazon's primary business is its online retail platform, where customers can purchase a vast array of products, including electronics, clothing, books, home goods, and much more. The company offers a convenient and user-friendly shopping experience, with features such as fast shipping, customer reviews, and personalized recommendations. In addition to its e-commerce platform, Amazon has diversified its business into various other areas. One of its notable ventures is Amazon Web Services (AWS), a comprehensive cloud computing platform that provides services such as storage, compute power, and database management to individuals and businesses. AWS has become a leader in the cloud computing industry, powering many websites and applications worldwide. Amazon has also developed its own consumer electronics, including the popular Amazon Kindle e-reader, Fire tablets, Fire TV streaming devices, and the Alexa-powered Echo smart speakers. The Alexa voice assistant, integrated into these devices, allows users to interact with their devices using voice commands, perform tasks, and access information. Furthermore, Amazon has expanded into media and entertainment. It operates Prime Video, a streaming service that offers a wide range of movies, TV shows, and original content. Amazon Music provides a platform for streaming and purchasing digital music, while Audible offers audiobooks and other audio content. The company's commitment to customer satisfaction and convenience is demonstrated by its membership program, Amazon Prime. Prime members receive various benefits, including free two-day shipping, access to streaming services, exclusive deals, and more.

Industry

It services, book publishers, retail, real estate, computer and electronic product manufacturing and software development

Company size

10,000+ Employees

Headquarters location

Seattle, WA, US