ML/AI Engineer Intern — Hardware & LLM Systems

llmda Inc.; Santa Clara (CA); Hybrid; USD 41,000 - 55,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

Gym on-site

401(k) Retirement Savings Plan

Job summary

llmda Inc. in Santa Clara, CA, is building a Generative AI-based semiconductor design platform. This internship offers hands-on work with our AI fabric powering hardware design, embedding AI into embedded systems using LLMs and RAG pipelines.

You will collaborate with core engineers, research, design, and deploy LLM-based solutions while bridging foundation models with engineering data. This internship emphasizes experimentation, cross-functional collaboration, and real impact on design

Qualifications

• Strong fundamentals in LLMs, NLP, and transformer architectures.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Experience with LLM orchestration tools (LangChain, LlamaIndex).
• Active pursuit of BS/MS/PhD in CS/AI/EE or related field.

Responsibilities

• Build Agentic Workflows: design, prototype, and implement multi-agent AI systems for evaluating and validating engineering artifacts.
• Model Optimization & RAG: fine-tune LLMs and optimize RAG pipelines for hardware contexts like Verilog and IP guides.
• Data Pipeline Development: ingest, clean, and structure engineering data for semantic engines.
• Evaluate & Iterate: develop metrics to ensure accuracy and reduce hallucinations in technical environments.
• Collaborate Cross-Functionally: partner with software architects, frontend engineers, and hardware experts.

Skills

LLMs

NLP

Transformer architectures

Python

PyTorch

TensorFlow

LangChain

LlamaIndex

Education

BS/MS/PhD in CS/AI/EE

Tools

Verilog

VHDL

AutoGen

CrewAI

Job description

llmda Inc. in Santa Clara, CA, is building a Generative AI-based semiconductor design platform. This internship offers hands-on work with our AI fabric powering hardware design, embedding AI into embedded systems using LLMs and RAG pipelines.

You will collaborate with core engineers, research, design, and deploy LLM-based solutions while bridging foundation models with engineering data. This internship emphasizes experimentation, cross-functional collaboration, and real impact on design