Meta designs and deploys its own AI systems. MTIA, the Meta Training and Inference Accelerator, is Meta's family of in-house AI accelerator ASICs, deployed in production across Meta's data centers and expanding workload coverage from ranking and recommendation to generative AI. The MTIA Software team is part of the AI & Compute Foundation organization. Because the hardware is ours, the software is ours too: we build the entire stack a chip vendor would normally supply: compiler and toolchain, runtime, kernel libraries, developer tooling, and deep PyTorch integration, co-designed with the silicon teams generation over generation.
The GenAI Frameworks org owns the layer where a model meets the machine. We take frontier models, including large language, multimodal and diffusion models, and make them run efficiently on MTIA. Our work determines how fast those models respond, how much context they can afford to hold, how many of them a given amount of silicon can serve, and how quickly a newly released model can be running and trusted in production.
We are hiring a Software Engineer to own a key area of GenAI inference on MTIA, spanning serving frameworks, distributed inference, graph-mode execution and compilation, and core PyTorch integration. This is a domain-expert role: you will set technical direction for your area, drive ambiguous multi-quarter programs across team boundaries, and own the quality bar for accuracy, stability, performance, and test coverage on custom silicon.
