Senior Systems ML Engineer — High-Performance AI Infra

Meta; Madison (WI); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta is seeking a Software Engineer for the Systems ML Engineering team in Madison, WI. You will design and optimize ML infrastructure spanning training, inference, and hardware-aware pipelines, collaborating with researchers and product teams to scale AI workloads serving billions of users.

You will implement high-performance components in C++ and Python, identify bottlenecks, and contribute to system design decisions while mentoring others on best practices for ML infrastructure.

Qualifications

• Bachelor's degree in CS/CE or equivalent practical experience.
• 6+ years in software engineering focused on ML systems or AI infrastructure.
• Experience with PyTorch, TensorFlow, or equivalent.
• Experience in distributed architectures for ML workloads.
• Proficiency in C++ and Python for performance-critical systems.
• Experience with profiling to identify bottlenecks.

Responsibilities

• Design, build, and optimize large-scale ML training and inference systems.
• Develop high-performance ML infrastructure components in C++ and Python.
• Identify and resolve performance bottlenecks across the ML stack.
• Architect trade-offs in ML system design for memory, compute, I/O.
• Collaborate with research and product teams to translate model needs into infra.
• Define system-level metrics and SLA for production ML serving.
• Lead design reviews and engineering standards for ML systems.
• Mentor engineers on ML infra and performance optimization.
• Drive AI-augmented development workflows to boost productivity.

Skills

Large-scale ML systems

C++

Python

Performance optimization

Profiling tools

Distributed computing

ML frameworks

System design

Hardware-aware optimization

Mentoring

Education

Bachelor's degree in Computer Science or related

Tools

CUDA

ROCm

MLIR

LLVM

XLA

Job description

Meta is seeking a Software Engineer for the Systems ML Engineering team in Madison, WI. You will design and optimize ML infrastructure spanning training, inference, and hardware-aware pipelines, collaborating with researchers and product teams to scale AI workloads serving billions of users.

You will implement high-performance components in C++ and Python, identify bottlenecks, and contribute to system design decisions while mentoring others on best practices for ML infrastructure.