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Internship Machine Learning Compiler Engineer Jobs in California

Nvidia

Senior Compiler Engineer - AI

Santa Clara, CA

$143K - $189K/yr

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 ...

Datapelago

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 ...

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 ...

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Internship Machine Learning Compiler Engineer information

What is the difference between Internship Machine Learning Compiler Engineer vs Internship Software Engineer?

AspectInternship Machine Learning Compiler EngineerInternship Software Engineer
FocusDeveloping and optimizing compilers for machine learning modelsDesigning, coding, and testing software applications across various domains
SkillsMachine learning, compiler design, programming (C++, Python)Programming, algorithms, software development
Work EnvironmentResearch labs, tech companies, AI-focused teamsTech companies, startups, software firms
Industry UsageAI, machine learning, deep learning industriesBroad software development across industries

Internship Machine Learning Compiler Engineers focus on creating and optimizing compilers for machine learning models, requiring knowledge of AI and compiler design. In contrast, Internship Software Engineers work on developing general software applications across various fields. Both roles involve programming skills but differ in their specialization and industry focus.

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Internship Machine Learning Compiler Engineer jobs near you
Infographic showing various Internship 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.

Senior Machine Learning Applications and Compiler Engineer, LPX

Nvidia

Santa Clara, CA • Hybrid

Full-time

Re-posted 16 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

7th of 245 rated software companies

Job description

We are now looking for a Senior Machine Learning Applications and Compiler Engineer!

NVIDIA is seeking engineers to develop algorithms and optimizations for our LPX inference and compiler stack. You will work at the intersection of large-scale systems, compilers, and deep learning, crafting how neural network workloads map onto future NVIDIA platforms. This is your chance to be part of something outstandingly innovative!

What you'll be doing:

  • Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization.

  • Define and implement mappings of large-scale inference workloads onto NVIDIA's systems.

  • Extend and integrate with NVIDIA's SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms.

  • Benchmark, profile, and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware.

  • Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points.

  • Prototype and evaluate new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors.

  • Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues.

What we need to see:

  • MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience, with 5 years of relevant experience.

  • Strong software engineering background with proficiency in systems level programming (e.g., C/C++ and/or Rust) and solid CS fundamentals in data structures, algorithms, and concurrency.

  • Hands on experience with compiler or runtime development, including IR design, optimization passes, or code generation.

  • Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations.

  • Familiarity with deep learning frameworks such as TensorFlow and PyTorch, and experience working with portable graph formats such as ONNX.

  • Solid understanding of parallel and heterogeneous compute architectures, such as GPUs, spatial accelerators, or other domain specific processors.

  • Strong analytical and debugging skills, with experience using profiling, tracing, and benchmarking tools to drive performance improvements.

  • Excellent communication and collaboration skills, with the ability to work across hardware, systems, and software teams.

  • Ideal candidates will have direct experience with MLIR based compilers or other multilevel IR stacks, especially in the context of graph based deep learning workloads.

Ways to stand out from the crowd:

  • Prior work on spatial or dataflow architectures, including static scheduling, pipeline parallelism, or tensor parallelism at scale.

  • Contributions to opensource ML frameworks, compilers, or runtime systems, particularly in areas related to performance or scalability.

  • Demonstrated research impact, such as publications or presentations at conferences like PLDI, CGO, ASPLOS, ISCA, MICRO, MLSys, NeurIPS, or similar.

  • Experience with large-scale AI distributed inference or training systems, including performance modeling and capacity planning for multi rack deployments.

#LI-Hybrid

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 for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 17, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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.

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

Year founded

1993

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