Senior Systems ML Engineer: High-Performance AI Infra

Meta; Cheyenne (WY); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta is seeking a Software Engineer for the Systems ML Engineering team to build and optimize ML infrastructure at massive scale. You will work on training and inference pipelines, hardware-aware optimizations, and end-to-end ML system design.

You will collaborate with researchers, platform engineers, and product teams to accelerate ML workloads and improve AI infrastructure serving billions of users, while mentoring teammates and driving reliability across production systems.

Qualifications

• Bachelor's degree in CS, CE, or related field.
• 6+ years of software engineering with ML systems focus.
• Experience with ML training/inference pipelines (PyTorch, TensorFlow).
• Experience with distributed computing for ML workloads.
• Proficient in C++ and Python for performance-critical systems.
• Experience profiling and optimizing ML systems.

Responsibilities

• Design, build, and optimize large-scale ML training/inference systems.
• Develop and maintain high-performance ML infra components.
• Identify and resolve performance bottlenecks across ML stack.
• Architect and evaluate trade-offs in ML system design.
• Collaborate with research and product teams on infra solutions.
• Define and track system-level metrics and SLIs for ML serving.
• Lead technical design reviews and set engineering standards.
• Mentor engineers on ML infrastructure best practices.
• Drive adoption of AI-augmented development workflows.
• Contribute to staged rollout strategies using feature flags.

Education

Bachelor's degree in Computer Science or related technical field

Tools

C++

Python

PyTorch

TensorFlow

CUDA

Job description

Meta is seeking a Software Engineer for the Systems ML Engineering team to build and optimize ML infrastructure at massive scale. You will work on training and inference pipelines, hardware-aware optimizations, and end-to-end ML system design.

You will collaborate with researchers, platform engineers, and product teams to accelerate ML workloads and improve AI infrastructure serving billions of users, while mentoring teammates and driving reliability across production systems.