Software Engineer, Systems ML Tooling

About the job

Meta is seeking a Software Engineer to join the MTIA (Meta Training & Inference Accelerator) Software Tooling team, which develops and maintains the tooling ecosystem for Meta's in-house AI accelerator ASICs. The Tooling team provides debugging, profiling, memory analysis, and monitoring capabilities for the whole MTIA Ecosystem, redefining ML accelerator tooling by leveraging Meta's full-stack ownership from silicon specs to fleet observability. In this role, you will design and build developer tools across the MTIA tooling ecosystem, with a primary focus on debugging, sanitizer technology, and fault isolation for AI workloads running on MTIA hardware at scale. You will work at the intersection of compilers, runtime, hardware, and ML frameworks, collaborating with cross-functional partners to deliver a high-quality developer experience for Meta's custom AI accelerators. This role can focus on either debugging and sanitizing technology or simulation infrastructure for pre-silicon and post-silicon model bring-up, depending on the candidate's background and team needs.

Responsibilities

Lead the design and development of MTIA's debugging and sanitizer tooling, including live debugging, core dump, and memory sanitizers for accelerator workloads Build debugging capabilities spanning graph-mode debugging, kernel-level diagnostics, and multi-rank fault isolation Contribute broadly to the MTIA SW tooling infrastructure — profiling, performance debugging, memory analysis, monitoring, and reliability analysis for training and inference workloads Own significant components end-to-end, from technical design through implementation, production rollout, and long-term maintenance Collaborate closely with the MTIA compiler, runtime, kernel, and hardware teams to instrument the software stack with the hooks, sanitizers, and debuggers required Drive AI-native tooling approaches, leveraging automation and LLM-guided diagnostics to reduce time-to-root-cause Partner with internal product teams across advertising, recommendations, and generative AI to understand developer pain points and prioritize tooling investments Communicate architectural decisions through design documents and cross-team reviews; share debugging methodology and best practices with engineers across the MTIA stack