Software Engineer, Systems Machine Learning

2 days ago
New York, United States Meta Full-time $121,992 - $181,000/year
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, build, and maintain developer tools that help engineers debug, profile, measure, and monitor 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.

Design and develop software tools across the MTIA tooling ecosystem, including debugging, profiling, performance analysis, memory analysis, and monitoring infrastructure

Own features and components end-to-end, from design through implementation, testing, and deployment

Build and improve tooling infrastructure that enables engineers to efficiently develop, test, and optimize AI workloads on MTIA hardware

Collaborate with the MTIA compiler, runtime, kernel, and hardware teams to integrate tooling hooks and support new platform capabilities

Proactively identify gaps in the tooling ecosystem and propose solutions to improve developer productivity

Contribute to technical design discussions, write design documents for medium-scope projects, and participate in code reviews

Partner with internal product teams across advertising, recommendations, and generative AI to understand developer pain points and improve tool usability

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience

2+ years of experience in software engineering, with exposure to systems software, developer tooling, or infrastructure

Proficiency in C++ and Python, including systems-level programming concepts

Experience working across multiple layers of a system stack (e.g., application, runtime, OS/driver, or hardware interfaces)

Track record of independently delivering software projects from design through production deployment

Experience debugging and resolving issues in complex software systems (e.g., using log analysis, stack traces, or system-level diagnostic tools)

Exposure to accelerator ecosystems (GPU/CUDA, TPU, custom ASICs) or heterogeneous computing environments

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Experience building developer tools such as debuggers, profilers, build systems, CLI tools, monitoring dashboards, or diagnostic utilities

Contributions to open-source projects demonstrating tooling or system software interest

Experience in using data-driven methods to evaluate tooling effectiveness and to prioritize improvements

Familiarity with Linux debugging and profiling tools (gdb, perf, strace, eBPF, valgrind) or similar diagnostic infrastructure

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Familiarity with ML frameworks (PyTorch, TensorFlow) or compiler infrastructure (LLVM, MLIR, TVM)

Experience with distributed systems debugging, profiling, or monitoring at scale

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

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