Senior Systems ML Engineer — Scalable AI Infrastructure

Meta; Jackson (MS); 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 the ML infrastructure powering Meta’s products at scale. You will work across the full stack from model training and inference pipelines to hardware-aware optimizations, collaborating with researchers, platform engineers, and product teams to accelerate ML workloads.

You will contribute to production reliability, performance tuning, and scalable ML systems that serve billions of users, with a strong

Qualifications

• Bachelor's degree or equivalent practical experience in a technical field.
• 6+ years of software engineering experience focused on ML systems.
• Experience with ML training/inference pipelines in PyTorch or TensorFlow.
• Experience with distributed architectures for ML workloads.
• C++ and Python for performance-critical systems.
• Experience profiling and optimizing ML/pipeline bottlenecks.

Responsibilities

• Design and build large-scale ML training and inference systems.
• Develop high-performance ML infrastructure components.
• Identify and resolve bottlenecks using profiling and benchmarking tools.
• Collaborate with research and product teams to translate ML requirements into infra solutions.
• Define and track system-level metrics and SLIs/SLOs for ML serving systems.
• Lead technical design reviews and engineering standards for ML infrastructure.
• Mentor engineers on ML infra best practices and optimization techniques.
• Drive AI-augmented development workflows to boost productivity.
• Contribute to staged rollout strategies using feature flags and experiments.

Skills

C++

Python

High-performance computing

Profiling & optimization

Distributed systems

Education

Bachelor's degree in CS/CE or related field

Tools

PyTorch

TensorFlow

CUDA

MLIR/LLVM

Job description

Meta is seeking a Software Engineer for the Systems ML Engineering team to build and optimize the ML infrastructure powering Meta’s products at scale. You will work across the full stack from model training and inference pipelines to hardware-aware optimizations, collaborating with researchers, platform engineers, and product teams to accelerate ML workloads.

You will contribute to production reliability, performance tuning, and scalable ML systems that serve billions of users, with a strong