Software Engineer, Systems ML

Meta; United States; À distance; USD 210 000 - 300 000; Plein temps

Résumé du poste

Meta is seeking a Software Engineer to join our Systems ML Engineering team. You will design and optimize ML infrastructure at massive scale, spanning model training, inference pipelines, and hardware-aware optimizations.

Collaborate with researchers, platform engineers, and product teams to accelerate AI workloads and improve efficiency for billions of users. You will work with PyTorch, TensorFlow, C++, Python, and GPU accelerators, contributing to a robust, scalable ML stack and responsible AI

Qualifications

• Bachelor's degree in a technical field or equivalent practical experience.
• 6+ years of software engineering with a focus on ML systems or high-performance computing.
• Experience building and optimizing ML training or inference pipelines with PyTorch or TensorFlow.
• Experience with distributed architectures and large-scale ML systems design.
• Proficient in C++ and Python for performance-critical code.
• Experience with GPU programming and accelerator kernel development.

Responsabilités

• Design and develop high-performance ML systems and infrastructure.
• Optimize ML training and inference pipelines across the full stack.
• Collaborate with researchers, platform engineers, and product teams to scale ML workloads.
• Profile, identify bottlenecks, and apply performance optimizations on AI accelerators.

Connaissances

ML systems

Distributed computing

C++ & Python

Profiling tools

GPU programming

AI infrastructure

Performance optimization

Ethical AI practices

Hardware-aware optimizations

Formation

Bachelor's degree in CS/CE or equivalent

Outils

PyTorch

TensorFlow

MLIR/LLVM/XLA

CUDA/ROCm

TVM

IREE

Description du poste

Meta is seeking a Software Engineer to join our Systems ML Engineering team, focused on building and optimizing the machine learning infrastructure that powers Meta's products at massive scale. In this role, you will design and develop high-performance ML systems, working across the full stack from model training and inference pipelines to hardware-aware optimizations. You will collaborate with researchers, platform engineers, and product teams to accelerate ML workloads and improve the efficiency of AI infrastructure that serves billions of users.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience6+ years of experience in software engineering with a focus on machine learning systems, AI infrastructure, or high-performance computing
• Experience developing and optimizing ML training or inference pipelines using frameworks such as PyTorch, TensorFlow, or equivalent
• Experience with distributed computing architectures and large-scale systems design for ML workloads
• Experience programming in C++ and Python for performance-critical systems
• Experience using profiling and performance analysis tools to identify and resolve bottlenecks in ML or compute-intensive systems
• Experience optimizing large-scale ranking and recommendation model inference on AI accelerator hardware
• Experience with hardware-software co-design, including numerics optimization and SIMD or vectorization techniques
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience with GPU programming using CUDA, ROCm, or equivalent hardware accelerator kernel development
• Experience with ML compiler technologies such as MLIR, LLVM, TVM, XLA, or IREE
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)