About The Position

Join our team as an intern to build the future of inference, GPU optimization and AI infrastructure. You'll work directly as a full-time engineer with the team to help define our technical direction and build the core systems that power our GPU optimization platform.

Requirements

  • 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.

Nice To Haves

  • 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.

Responsibilities

  • Build scalable infrastructure for AI model training and inference
  • Lead technical decisions and architecture choices
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