Senior Systems ML Engineer: High-Performance AI Infra

Meta; Tallahassee (FL); On-site; USD 154,000 - 217,000; Full time

Job summary

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

Qualifications

• 6+ years of software engineering focused on ML systems.
• Experience with PyTorch, TensorFlow, or equivalent in training/inference pipelines.
• Proficient in C++ and Python for performance-critical systems.

Responsibilities

• Design, build, and optimize large-scale ML training and inference systems, including distributed computing frameworks and hardware-accelerated pipelines.
• Identify and resolve performance bottlenecks using profiling, instrumentation, and benchmarking tools.
• Architect and evaluate trade-offs in ML system design, including memory bandwidth and compute utilization.
• Partner with research and product teams to translate ML model requirements into infrastructure solutions.
• Define and track system-level metrics and SLIs to maintain production reliability of ML serving systems.
• Lead technical design reviews and contribute to engineering standards for ML systems.
• Mentor engineers on ML infrastructure best practices and performance optimization.

Tools

C++

Python

PyTorch

TensorFlow

CUDA

ROCm

MLIR

LLVM

TVM

XLA

IREE

Job description

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