Systems ML Engineer — AI Infra & Hardware Acceleration

Meta; Austin (TX); On-site; USD 184,000 - 257,000; Full time

Job summary

Meta is seeking an AI Software Engineer to work on AI infrastructure and ML systems across multiple locations. You will apply hardware acceleration techniques to optimize intelligent ML systems that power Meta’s products and experiences, while setting goals and driving efficiency across teams.

You will mentor engineers, define use cases, benchmarks, and ensure data-driven decision making, collaborating with partners to deliver measurable impact in a fast-paced environment.

Qualifications

• Bachelor's degree in CS/CE or equivalent practical experience.
• Specialized experience in ML infra domains such as hardware accelerators, GPU architecture, ML compilers, AI infrastructure, or ML systems.
• Experience developing AI-system infrastructure or AI algorithms in C/C++ or Python.

Responsibilities

• Apply AI infrastructure and hardware acceleration techniques to build and optimize ML systems.
• Set goals for project impact, AI design, and developer efficiency across teams.
• Collaborate with partners to deliver impact through data-driven analysis.
• Drive large efforts across multiple teams to advance initiatives.
• Define use cases and develop benchmarks to evaluate approaches.
• Understand how ML infrastructure interacts with surrounding systems.
• Mentor engineers and researchers to improve engineering quality.

Skills

AI infrastructure

Hardware acceleration

ML systems

AI system design

Infrastructure efficiency

Data-driven analysis

Cross-team leadership

Use cases

Benchmarks

ML infra interaction

Mentoring

Education

Bachelor's degree in Computer Science or related field

Tools

PyTorch

Job description

Meta is seeking an AI Software Engineer to work on AI infrastructure and ML systems across multiple locations. You will apply hardware acceleration techniques to optimize intelligent ML systems that power Meta’s products and experiences, while setting goals and driving efficiency across teams.

You will mentor engineers, define use cases, benchmarks, and ensure data-driven decision making, collaborating with partners to deliver measurable impact in a fast-paced environment.