Software Engineering Manager, AI Systems & HPC

Takadam; Menlo Park (CA); On-site; USD 177,000 - 251,000; Full time

Benefits offered by this job

Health insurance

Paid leave

Professional development

Equity

Job summary

Meta is seeking an Software Engineering Manager for the AI Systems Co-Design team, based in Menlo Park. You will lead HPC and AI infrastructure efforts at the intersection of hardware and software, delivering compute systems for large-scale models like LLaMA and DLRM.

You’ll collaborate with academia and industry, drive performance optimization, and guide cross-functional teams across engineering, research, and product groups to push the boundaries of scalable AI infrastructure.

Qualifications

• Proven leadership in high-performance computing (HPC) and AI/ML systems.
• Experience with communication libraries (NCCL, RCCL, UCC, MPI).
• Background in GPU/ASIC kernel development (CUDA, ROCm).
• Skilled in distributed systems, systems architecture, and performance optimization.
• Experience managing large-scale engineering programs and cross-functional collaboration.

Responsibilities

• Lead the AI Systems Co-Design team, aligning hardware, software, and AI infra.
• Deliver high-performance compute systems to support large-scale AI models like LLaMA and DLRM.
• Collaborate with top academic institutions and industry partners to advance infrastructure.
• Shape Meta’s AI infrastructure strategy through cross-functional leadership.

Skills

HPC leadership

AI/ML systems

Distributed systems

Systems architecture

Performance optimization

Cross-functional collaboration

Large-scale program management

Team leadership

Tools

CUDA

ROCm

NCCL

RCCL

UCC

MPI

Job description

Meta is seeking an Software Engineering Manager for the AI Systems Co-Design team, based in Menlo Park. You will lead HPC and AI infrastructure efforts at the intersection of hardware and software, delivering compute systems for large-scale models like LLaMA and DLRM.

You’ll collaborate with academia and industry, drive performance optimization, and guide cross-functional teams across engineering, research, and product groups to push the boundaries of scalable AI infrastructure.