Senior Systems ML Engineer - High-Performance Infra

Meta; Baton Rouge (LA); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta seeks a Software Engineer for its Systems ML Engineering team to design and build high-performance ML infrastructure across training and inference pipelines. You will optimize ML systems with hardware-aware techniques and collaborate with researchers, platform engineers, and product teams to accelerate AI workloads serving billions of users.

The role requires strong C++ and Python, distributed systems experience, and a track record of profiling and optimizing performance at scale.

Qualifications

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

Responsibilities

• Design, build, and optimize large-scale ML training and inference systems
• Develop and maintain high-performance ML infrastructure components in C++ and Python
• Identify and resolve performance bottlenecks across the ML stack
• Architect and evaluate trade-offs in ML system design (memory bandwidth, compute, I/O)
• Partner with research/product teams to translate ML requirements into infrastructure solutions
• Define and track system-level metrics and reliability of ML serving systems
• Lead technical design reviews and engineering standards for ML systems
• Mentor engineers on ML infrastructure and performance optimization
• Drive adoption of AI-augmented development workflows
• Contribute to staged rollout strategies using feature flags and experimentation

Job description

Meta seeks a Software Engineer for its Systems ML Engineering team to design and build high-performance ML infrastructure across training and inference pipelines. You will optimize ML systems with hardware-aware techniques and collaborate with researchers, platform engineers, and product teams to accelerate AI workloads serving billions of users.

The role requires strong C++ and Python, distributed systems experience, and a track record of profiling and optimizing performance at scale.