Software Engineer, ML Compiler and Performance

Meta•Sunnyvale, CA

4d•$154,003 - $217,000

About The Position

Meta's Training and Inference Accelerators (MTIA) team is developing novel HW to enable efficient execution of AI training and inference workloads. In this role, you will have end-to-end responsibility for the performance of in-production AI models in their transition from stock HW to MTIA chips, with a focus on models that require multi-node compute. To learn more about MTIA, explore the links below: - https://ai.meta.com/blog/meta-training-inference-accelerator-AI-MTIA/ - https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/ - https://dl.acm.org/doi/full/10.1145/3695053.3731409

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Experience developing and deploying optimizations at the level of PyTorch/Aten or comparable stacks

Nice To Haves

A PhD degree and 2+ years in-domain experience
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience optimizing multi-node distributed compute
Experience optimizing runtimes and/or kernels for accelerator platforms
A Master's degree and 4+ years of in-domain experience
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Responsibilities

Identifying bottlenecks and quantifying opportunities for improving performance
In-depth, end-to-end performance analysis and reporting
Developing optimizations to address identified bottlenecks
Optimizing compute/communication overlap
Work closely with other compiler teams as well as client teams (Recommendation Systems, Generative AI, etc)