Systems ML Engineer: High-Performance AI Infra

Meta; City of Albany (NY); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta is seeking a Software Engineer for the Systems ML Engineering team in New York. You will design and build high-performance ML infrastructure spanning training to inference, with hardware-aware optimizations and full-stack responsibilities.

You’ll collaborate with researchers, platform engineers, and product teams to accelerate ML workloads and improve AI infrastructure serving billions of users, while mentoring others and driving adoption of AI-assisted development workflows.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or a related technical field.
• 6+ years of software engineering experience with a focus on machine learning systems or AI infrastructure.
• Experience with ML training/inference pipelines using PyTorch, TensorFlow, or equivalents.
• Experience with distributed computing architectures for ML workloads.

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 trade-offs in ML system design focusing on memory bandwidth and I/O throughput.
• Collaborate with research and product teams to implement ML model requirements into infrastructure solutions.

Skills

C++

Python

Distributed systems

Profiling tools

PyTorch

TensorFlow

Performance optimization

ML infrastructure

Code profiling

Hardware acceleration

Education

Bachelor's degree in CS or related field

Tools

CUDA

ROCm

MLIR

LLVM

XLA

Job description

Meta is seeking a Software Engineer for the Systems ML Engineering team in New York. You will design and build high-performance ML infrastructure spanning training to inference, with hardware-aware optimizations and full-stack responsibilities.

You’ll collaborate with researchers, platform engineers, and product teams to accelerate ML workloads and improve AI infrastructure serving billions of users, while mentoring others and driving adoption of AI-assisted development workflows.