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Junior Machine Learning Compiler Engineer Jobs in San Ramon, CA

General Motors

Senior ML Compiler Engineer

San Francisco, CA · On-site

$123K - $169K/yr

If you want your compiler andkernelsworktodirectlyinfluencehow automated vehicles understand and ... Experience developing and deploying machine learning models Compensation: The compensation ...

General Motors

Senior ML Compiler Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

If you want your compiler andkernelsworktodirectlyinfluencehow automated vehicles understand and ... Experience developing and deploying machine learning models Compensation: The compensation ...

SambaNova

Compiler Architect

San Jose, CA · On-site

$210K - $280K/yr

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 ...

SambaNova

Senior AI Performance Engineer

San Jose, CA

$143K - $189K/yr

Collaborate with machine learning, compiler, runtime, and hardware teams to deliver co-designed ... Bachelor's or higher degree in computer science, electrical engineering, or a related field (e.g ...

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All JobsMachine Learning Compiler Engineer JobsJunior Machine Learning Compiler Engineer JobsJunior Machine Learning Compiler Engineer Jobs in San Ramon, CA

Junior Machine Learning Compiler Engineer information

See San Ramon, CA salary details

$37.4K

$80.2K

$122.4K

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

As of Sep 9, 2026, the average yearly pay for junior machine learning compiler engineer in San Ramon, CA is $80,237.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,200.00 and $89,400.00 per year, depending on experience, location, and employer.

What does a junior machine learning compiler engineer do?

A Junior Machine Learning Compiler Engineer helps design, develop, and optimize compilers for machine learning models. Their work involves translating high-level machine learning code into efficient low-level code that can run on various hardware platforms, such as CPUs, GPUs, or specialized AI chips. They often collaborate with software engineers and data scientists to ensure that machine learning workloads run efficiently and correctly. This role typically involves programming, debugging, and performance tuning, often using languages like C++, Python, and specialized frameworks.

What are typical projects and responsibilities for a junior machine learning compiler engineer in a collaborative team setting?

As a Junior Machine Learning Compiler Engineer, you can expect to work on projects that focus on optimizing machine learning models for performance and deployment across various hardware platforms. Typical responsibilities include assisting in developing and debugging compiler passes, implementing optimizations, and contributing to code reviews. You'll frequently collaborate with senior engineers, data scientists, and hardware specialists to ensure that models are efficiently translated and executed. This role offers valuable learning opportunities through hands-on coding, exposure to state-of-the-art ML frameworks, and regular team meetings for knowledge sharing and mentorship.

What are the key skills and qualifications needed to thrive as a junior machine learning compiler engineer, and why are they important?

To thrive as a Junior Machine Learning Compiler Engineer, you need a solid background in computer science fundamentals, programming (especially C++ and Python), and foundational knowledge of machine learning and compiler theory. Familiarity with frameworks and tools such as LLVM, TensorFlow, MLIR, and version control systems is typically required, along with a relevant bachelor’s or master’s degree. Strong problem-solving abilities, attention to detail, and effective teamwork and communication skills set standout candidates apart. These skills and qualities are crucial for efficiently optimizing machine learning models for various hardware targets and collaborating on innovative compiler solutions.

What is the difference between Junior Machine Learning Compiler Engineer vs Data Scientist?

AspectJunior Machine Learning Compiler EngineerData Scientist
Required CredentialsBachelor's in Computer Science, Software Engineering, or related field; knowledge of compiler design and ML frameworksBachelor's or higher in Data Science, Statistics, Computer Science, or related field; strong analytical skills
Work EnvironmentSoftware development teams, focusing on compiler optimization for ML modelsData analysis teams, focusing on data interpretation and model development
Employer & Industry UsageTech companies, AI startups, hardware firmsTech firms, finance, healthcare, research institutions

The Junior Machine Learning Compiler Engineer primarily focuses on developing and optimizing compilers for machine learning models, requiring programming and compiler knowledge. In contrast, a Data Scientist analyzes data, builds models, and provides insights. Both roles are essential in AI and tech industries but differ in technical focus and daily tasks.

What are popular job titles related to Junior Machine Learning Compiler Engineer jobs in San Ramon, CA?

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What cities near San Ramon, CA are hiring for Junior Machine Learning Compiler Engineer jobs?

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Junior Machine Learning Compiler Engineer jobs near you
Infographic showing various Junior Machine Learning Compiler Engineer job openings in San Ramon, CA as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $80,237 per year, or $38.6 per hour.

Mid-Level Machine Learning Engineer

San Jose, CA • On-site

TetraMem - Accelerate The World
Computer and Peripheral Equipment Manufacturing • 11 - 50 employees
Learn more about TetraMem - Accelerate The Worldexternal

$110K - $200K/yr

Full-time

Re-posted 24 days ago

Job description

Responsibilities:

  • Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
  • Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
  • Work closely with hardware and software teams to integrate ML models into production systems.
  • Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
  • Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
  • Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
  • Provide technical leadership and mentorship to junior engineers.
  • Publish research findings, present at conferences, and contribute to open-source projects when applicable.


Requirements:

  • 5+ years of experience or PhD in Computer Science, Electrical Engineering, or related fields.
  • Strong experience in machine learning, with a focus on edge AI and lightweight model deployment.
  • Expertise in ML frameworks such as PyTorch, TensorFlow, JAX.
  • Proficiency in programming languages such as C/C++, Python, and experience with ML model optimization.
  • Ability to work independently and collaboratively in a fast-paced startup environment.
  • Ability to provide mentorship, technical guidance, and career development support to junior engineers and interns.


Experience in one or more of the following areas considered a strong plus:

  • Understanding of ML compiler and runtime design.
  • Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
  • Familiarity with hardware acceleration techniques.
  • Experience in embedded system development.

Salary Range: $110,000 - $200,000 / year

TetraMem celebrates diversity and is committed to creating an inclusive environment for all employees. We are proud to be an Equal Opportunity Employer and welcome applicants from all backgrounds. Qualified candidates will receive consideration for employment without regard to race, color, religion, creed, sex, gender identity or expression, sexual orientation, national origin, ancestry, age, marital status, medical condition, disability, genetic information, military or veteran status, or any other characteristic protected by applicable federal, state, or local law.
TetraMem is committed to providing reasonable accommodations to qualified applicants with disabilities throughout the recruitment process. Applicants requiring accommodation may contact Human Resources for assistance.
To ensure a fair, consistent, and efficient hiring process, all candidates must apply through TetraMems official ClearCompany Applicant Tracking System (ATS). Applications submitted through the ATS allow our hiring team to evaluate candidates using a standardized process and ensure timely communication throughout the recruitment process. To promote equal consideration for all applicants, applications submitted outside of the ClearCompany ATS, including direct emails, LinkedIn messages, or unsolicited submissions to employees, may not be reviewed or considered.
We encourage all interested candidates to apply through the official TetraMem Careers page.

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