Senior ML Systems Engineer - High-Performance AI Infra

Meta; United States; Remote; USD 210,000 - 300,000; Full time

Job summary

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.

Responsibilities

• 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.

Skills

ML systems

Distributed computing

C++ & Python

Profiling tools

GPU programming

AI infrastructure

Performance optimization

Ethical AI practices

Hardware-aware optimizations

Education

Bachelor's degree in CS/CE or equivalent

Tools

PyTorch

TensorFlow

MLIR/LLVM/XLA

CUDA/ROCm

TVM

IREE

Job description

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