Founding Research Engineer, AI-Driven Compilation

About SF Tensor

At The San Francisco Tensor Company, we believe the future of AI and high-performance computing depends on rethinking the entire software and infrastructure stack. Today's developers face bottlenecks across hardware, cloud, and code optimization that slow progress before ideas can reach their full potential. Our mission is to remove those barriers and make compute faster, cheaper, and universally portable.

We are building a Kernel Optimizer that automatically transforms code into its most efficient form, combined with Tensor Cloud for adaptive, cross-cloud compute and Emma Lang, a new programming language for high-performance, hardware-aware computation. Together, these technologies reinvent the foundations of AI and HPC.

SF Tensor is proudly backed by Susa Ventures and Y Combinator, as well as a group of angels including Max Mullen and Paul Graham as well as founders and executives of NeuraLink, Notion and AMD. We are partnering with researchers, engineers, and organizations who share our belief that the next breakthroughs in AI require breakthroughs in compute.

About the Role

We're building a next-generation AI compiler that uses machine learning to optimize machine learning. As a Founding Research Engineer focused on AI-driven compilation, you'll develop the agentic and reinforcement learning systems that guide our compiler's optimization decisions.

This is a research-heavy role right at the intersection of compilers and ML. You'll design learned systems that can explore huge optimization spaces, discover new code transformations, and keep improving compilation quality over time.

What You'll Do

• Design and implement RL-based systems for compiler optimization (things like phase ordering, tile size selection, scheduling decisions, and fusion strategies)