Wafer Verified
Digital Health, Healthtech, Software Development, Mobile Applications
Member Of Technical Staff (Winter Intern)
San Francisco, California, United StatesOnsiteInternshipStaff$6,000–$10,000 /yrPosted 2 months agoHidden Gem · YC Startup
Role summary
This internship focuses on building the future of inference, GPU optimization, and AI infrastructure. Interns will work directly with the team to define technical direction and develop core systems for a GPU optimization platform. Key responsibilities include building scalable infrastructure for AI model training and inference, and contributing to technical decisions and architecture choices. Required technical expertise includes GPU fundamentals (architectures, CUDA, parallel computing), deep learning frameworks (PyTorch, TensorFlow, JAX), LLM knowledge (training, fine-tuning, prompting, evaluation), and systems engineering skills in C++, Python, and potentially Rust/Go.
Join our team to build the future of inference, GPU optimization and AI infrastructure. You'll work directly with the team to define our technical direction and build the core systems that power our GPU optimization platform.
### **What You'll Do**
* Build scalable infrastructure for AI model training and inference
* Lead technical decisions and architecture choices
### **What We Look For**
#### **Core Technical Expertise**
* GPU Fundamentals: Deep understanding of GPU architectures, CUDA programming, and parallel computing patterns.
* Deep Learning Frameworks: Proficiency in PyTorch, TensorFlow, or JAX, particularly for GPU-accelerated workloads.
* LLM/AI Knowledge: Strong grounding in large language models (training, fine-tuning, prompting, evaluation).
* Systems Engineering: Proficiency in C++, Python, and possibly Rust/Go for building tooling around CUDA.
### **Ideal Background**
* Publications or open-source contributions in inference GPU computing or ML/AI for code are a plus.
* Hands-on experience with large-scale experiments, benchmarking, and performance tuning.
### **What You'll Do**
* Build scalable infrastructure for AI model training and inference
* Lead technical decisions and architecture choices
### **What We Look For**
#### **Core Technical Expertise**
* GPU Fundamentals: Deep understanding of GPU architectures, CUDA programming, and parallel computing patterns.
* Deep Learning Frameworks: Proficiency in PyTorch, TensorFlow, or JAX, particularly for GPU-accelerated workloads.
* LLM/AI Knowledge: Strong grounding in large language models (training, fine-tuning, prompting, evaluation).
* Systems Engineering: Proficiency in C++, Python, and possibly Rust/Go for building tooling around CUDA.
### **Ideal Background**
* Publications or open-source contributions in inference GPU computing or ML/AI for code are a plus.
* Hands-on experience with large-scale experiments, benchmarking, and performance tuning.