What the role involves
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 they ask for
About Wafer
Wafer builds AI agents that work as autonomous performance engineers, optimizing GPU kernels for AI inference. Our product is serverless and dedicated inference for the world’s fastest open source LLMs, achieved by Wafer's autonomous performance engineers.
Full Wafer profile