Hyperbolic Labs is on a mission to democratize AI by breaking down the barriers to computing power with our Open-Access AI Cloud. By making better use of idle computing resources across the globe, we offer an innovative GPU marketplace and AI inference service that promise affordability and accessibility for all. As pioneers at the intersection of AI and open-source technology, we believe in an open future where AI innovation is limited only by imagination, not by access to resources. We're looking for forward-thinking individuals who share our passion for making AI universally accessible, secure, and affordable. Join us in building a platform that empowers innovators everywhere to turn their visionary AI projects into reality. As we prepare for growth with our seed round, backed by industry leaders, our team—led by co-founders with PhDs in AI, Math, and Computer Science—is poised to redefine computing.
About The Role:
We're on the lookout for an AI Engineer to join us in shaping the future of decentralized AI. In this role, you'll work alongside our Engineering team to develop our decentralized AI platform. This role requires the candidate to reside in the United States. Team members are expected to work in-person in the San Francisco Bay Area at least 75% of the time.
Who You Are:
You are the ideal candidate if you:
- Have a strong foundation in software engineering and a history of leading or significantly contributing to machine learning system projects.
- Have worked on distributed inference/fine-tuning for LLMs and diffusion models
- Have practical skills in training, fine-tuning, and deploying neural networks.
- Understand ML performance profiling and optimization, including graph-level optimizations.
- Are familiar with common neural network architectures and operators.
Preferred Qualifications:
- Knowledge of ML compilers, with TVM experience being a significant advantage.
- Experience with GPU kernels, CUDA, NCCL, Pytorch, Tensorflow.
- Understanding P2P networks or worked with distributed cloud services and microservices
