Staff ML Systems Engineer - Scalable AI Infrastructure

Meta; Menlo Park (CA); On-site; USD 183,997 - 257,000; Full time

Job summary

Meta seeks a Staff Software Engineer for the Systems ML Engineering team to architect and own ML infrastructure that powers Meta's production AI workloads. You will shape distributed training, model serving, and platform tooling across a demanding production fleet.

You will lead design initiatives, optimize performance, and collaborate with ML researchers and engineers to translate requirements into robust, scalable systems.

Qualifications

• Bachelor's degree or equivalent practical experience in CS/engineering.
• 8+ years of software eng experience focusing on systems software, distributed computing, or ML infrastructure.
• Experience designing and implementing large-scale distributed systems.
• Experience with performance analysis and optimization of compute-intensive workloads.
• Leadership of end-to-end tech projects across teams.
• Experience with C++, Python, or equivalent languages.

Responsibilities

• Design and implement scalable ML systems infrastructure components (distributed training frameworks, model serving, ML platform tooling).
• Lead technical design and architecture for major ML infrastructure initiatives.
• Identify and resolve performance bottlenecks in distributed training and inference systems.
• Define and drive service level objectives, dashboards, and incident runbooks for ML services.
• Mentor engineers on ML systems best practices and distributed computing patterns.
• Drive adoption of engineering standards, testing, feature flagging, and monitoring.

Skills

Distributed systems

C++

Python

ML infrastructure

Performance optimization

Leadership/mentorship

Education

Bachelor's degree in Computer Science/Engineering or related field

Tools

Model serving systems

Training orchestration

Job description

Meta seeks a Staff Software Engineer for the Systems ML Engineering team to architect and own ML infrastructure that powers Meta's production AI workloads. You will shape distributed training, model serving, and platform tooling across a demanding production fleet.

You will lead design initiatives, optimize performance, and collaborate with ML researchers and engineers to translate requirements into robust, scalable systems.