Learning Management System Intern Jobs in California

About the Role
We are seeking a Software Intern to support the development of tools, frameworks, and applications that enable neural network models to run efficiently on TetraMems novel analog compute-in-memory chips. This is an exciting opportunity to work at the intersection of software, hardware, and artificial intelligence.

Responsibilities

• Develop, optimize, and maintain Python and C++ software for neural network model compression, conversion, deployment, and runtime environments.
• Design and implement tools to improve machine learning model efficiency, including quantization, pruning, graph optimization, and memory reduction techniques.
• Analyze and adapt machine learning models to improve compatibility, performance, and power efficiency on Compute-in-Memory (CIM) and Analog AI hardware architectures.
• Develop software frameworks, APIs, and runtime components to support end-to-end AI model deployment on edge AI and embedded systems.
• Perform software design, implementation, debugging, testing, and performance profiling to optimize system throughput, latency, and resource utilization.
• Collaborate closely with AI researchers, hardware engineers, and system architects to validate hardware-software co-design and system-level functionality.
• Evaluate emerging machine learning models, algorithms, and deployment techniques to enhance AI inference performance on next-generation semiconductor platforms.
• Participate in code reviews, establish software quality standards, and contribute to automated testing, continuous integration, and technical documentation.
• Troubleshoot software and system issues across the AI stack, including model conversion pipelines, runtime environments, and hardware acceleration frameworks.
• Contribute to the development of innovative AI computing technologies and support research activities through technical presentations, publications, patents, and cross-functional collaboration.

Qualifications
Currently pursuing a degree in Computer Science, Electrical Engineering, or related field
Solid programming experience in Python, C++, or similar
Understanding of data structures, algorithms, and software architecture
Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch) is a plus
Strong problem-solving skills and eagerness to learn

Compensation

Hourly Rate: $35.00 $45.00 per hour (USD)

The final compensation will be determined based on the candidates education, experience, technical skills, and overall qualifications.

Work Location: This position is based on-site at TetraMems U.S. office in San Jose, California.

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.