Software Engineer, Systems ML

Meta; United States; Híbrido; USD 300.000 - 420.000; Jornada completa

Descripción de la vacante

Meta is seeking a principal-level Software Engineer to drive strategy and execution across our Systems ML Engineering organization. You will define the architectural foundations that power large-scale machine learning infrastructure, spanning training systems, inference pipelines, ML compilers, high-performance computing frameworks, and on-device optimization.

You will identify and solve the hardest cross-system ML infrastructure challenges, shaping multi-year roadmaps and amplifying engineering

Formación

• Bachelor's degree or equivalent practical experience.
• 12+ years in software engineering with ML systems specialization.
• Experience delivering large-scale ML training/inference infra with cross-team impact.
• Experience leading multi-year cross-functional technical initiatives.
• Strong C++, Python, or CUDA development.
• Ability to influence direction across teams through proposals and reviews.
• Commitment to responsible, ethical AI practices.

Responsabilidades

• Define architectural foundations for ML infrastructure.
• Drive multi-year roadmaps for training, inference, and AI tooling.
• Lead cross-functional initiatives across teams and stakeholders.
• Advance high-performance ML systems, AI efficiency, and reliability.

Conocimientos

ML systems

C++

Python

CUDA

GPU architecture

AI infrastructure

ML frameworks

Cross-functional leadership

Educación

Bachelor's degree in Computer Science or related field

Herramientas

MLIR

XLA

TVM

Descripción del empleo

Meta is seeking a principal-level Software Engineer to drive technical strategy and execution across our Systems ML Engineering organization. In this role, you will define the architectural foundations that power large-scale machine learning infrastructure, spanning training systems, inference pipelines, ML compilers, high-performance computing frameworks, and on-device optimization. You will identify and solve the hardest cross-system ML infrastructure challenges, shape multi-year technical roadmaps, and amplify the impact of engineering teams through AI-native workflows and deep systems expertise. This is a role for engineers who identify problems others miss and drive them to resolution at organizational scale.

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience12+ years of experience in software engineering with deep specialization in one or more ML systems domains including AI infrastructure, ML compilers, high-performance computing, GPU architecture, ML frameworks, or on-device optimizationExperience architecting and delivering large-scale ML training or inference infrastructure that has had measurable impact across multiple engineering organizationsExperience leading multi-year cross-functional technical initiatives, including defining metrics, managing dependencies, and driving execution across organizational boundariesExperience developing high-performance ML systems infrastructure in C++, Python, or CUDA, including work at the intersection of hardware and softwareExperience influencing technical direction and engineering practices across multiple teams through written proposals, design reviews, and stakeholder alignment Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologiesExperience contributing to industry-wide ML systems efforts through publications, open-source projects, or standards bodiesExperience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)Track record of applying AI tools and automation to redesign engineering workflows, with demonstrated efficiency or quality improvements at organizational scaleExperience with ML compiler stacks such as MLIR, XLA, or TVM, or with hardware-software co-design for custom ML acceleratorsExperience defining and operationalizing reliability, performance, and correctness standards for distributed ML training or large-scale inference systems