Senior Systems ML Engineer — Scalable AI Infra

Meta; Nashville (TN); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta is seeking a Software Engineer for the Systems ML Engineering team to design and optimize ML infrastructure at massive scale. You will work across the full stack from model training to inference pipelines, optimizing hardware-aware performance and reliability.

You will collaborate with researchers and product teams to accelerate ML workloads, define metrics, and lead design reviews while mentoring peers in ML infrastructure best practices.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 6+ years of software engineering focusing on ML systems, AI infrastructure, or high-performance computing.
• Experience with ML training/inference pipelines using PyTorch, TensorFlow, or equivalent.
• Experience with distributed computing architectures for ML workloads.
• Experience programming in C++ and Python for performance-critical systems.
• Experience using profiling and performance analysis tools to optimize bottlenecks.

Responsibilities

• Design, build, and optimize large-scale ML training and inference systems, including distributed computing frameworks and hardware-accelerated pipelines.
• Develop and maintain high-performance ML infrastructure components in C++ and Python for reliability and low latency.
• Identify and resolve performance bottlenecks across the ML stack using profiling and benchmarking tools.
• Architect and evaluate trade-offs in ML system design considering memory bandwidth, compute, and I/O throughput.
• Collaborate with research and product teams to translate ML model requirements into efficient infrastructure solutions.

Skills

ML systems design

C++

Python

Performance profiling

Distributed computing

System metrics

Leadership & mentoring

Experimentation workflows

Feature flags

CUDA

Education

Bachelor's degree in CS/Engineering

Tools

CUDA

ROCm

MLIR/LLVM

Job description

Meta is seeking a Software Engineer for the Systems ML Engineering team to design and optimize ML infrastructure at massive scale. You will work across the full stack from model training to inference pipelines, optimizing hardware-aware performance and reliability.

You will collaborate with researchers and product teams to accelerate ML workloads, define metrics, and lead design reviews while mentoring peers in ML infrastructure best practices.