Member Of Technical Staff (Summer Intern)

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. You will build scalable infrastructure for AI model training and inference and lead technical decisions and architecture choices. We look for deep understanding of GPU architectures, CUDA programming, parallel computing patterns, proficiency in PyTorch, TensorFlow, or JAX, strong grounding in large language models, and proficiency in C++, Python, Rust/Go for building tooling around CUDA.

Must Have

• Build scalable infrastructure for AI model training and inference.
• Lead technical decisions and architecture choices.
• Deep understanding of GPU architectures, CUDA programming, and parallel computing patterns.
• Proficiency in PyTorch, TensorFlow, or JAX for GPU-accelerated workloads.
• Strong grounding in large language models (training, fine-tuning, prompting, evaluation).
• Proficiency in C++, Python, and possibly Rust/Go for building tooling around CUDA.

Perks & Benefits

• Will sponsor

About the role

Skills: Torch/PyTorch, C++, Python, TypeScript, CUDA

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.