Andrew Aikawa is a CTO and co-founder focused on modernizing GPU infrastructure and squeezing more ROI out of AI training workloads, currently leading engineering at Trainy (YC S23). With a PhD in Physics from UC Berkeley and nine years of hands-on experience, he blends deep research pedigree with practical MLOps and distributed training expertise. He’s shipped production-scale training and serving pipelines—running large distributed training on hundreds of A100s, optimizing custom CUDA/Torch ops, and deploying models via Triton for real-time inference. An active open-source contributor, Andrew has improved SkyPilot’s multi-cloud and Kubernetes GPU integrations, enabling easier, cost-efficient AI job execution across clouds. He’s particularly interested in tooling that boosts multinode throughput, and brings an uncommon mix of low-level GPU systems work and applied ML production experience.
9 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at University of California, Berkeley
SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 15+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.
Role in this project:
Cloud Engineer / Infrastructure Engineer
Contributions:43 reviews, 25 PRs, 95 comments in 1 year 6 months
Contributions summary:Andrew's commits primarily focus on integrating and supporting the SkyPilot project with different cloud platforms, specifically Paperspace and Kubernetes. They were actively involved in adding and improving Paperspace cloud integration by implementing features such as launch, stop, and autodown functionality. Moreover, the user contributed to improvements related to GPU feature discovery in Kubernetes, and updated core parameters for Kubernetes, and generally improved the integration of SkyPilot with Kubernetes. Additionally, they addressed issues such as improving timeouts and correcting SSH setups.
Trainy Zep: Run LLMs, AI, and Batch jobs on reliably on k8s. Easy interface for running on Trainy Konduktor
Contributions:4 releases, 2 reviews, 17 PRs in 1 year 7 months
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