Senior AI Infrastructure Engineer - ML Accelerators

Socket.dev; Sunnyvale (CA); On-site; USD 207,000 - 300,000; Full time

Benefits offered by this job

High-value equity

Job summary

Socket.dev is seeking a software engineer with deep expertise in C++, GPU programming, and distributed systems to help scale enterprise AI infrastructure. The role emphasizes leadership, architecture, and collaboration with hardware teams to deliver reliable accelerator software across TPUs and GPUs.

The ideal candidate has multi-year experience in large-scale systems, machine learning infrastructure, and cross-functional programs, with a record of technical leadership and project direction.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of experience programming in C++.
• 5 years of experience testing and launching software products.
• 5 years of building large-scale infrastructure, distributed systems or networks, or compute/storage/hardware architecture.
• 3 years of experience with software design and architecture.
• Experience with GPU programming, ML infrastructure, AI platform, ML optimization, and systems infrastructure.

Responsibilities

• Define architecture and long-term technical roadmap for accelerator software stacks.
• Collaborate with HW engineering to design, test, deploy, and debug low-level software across the stack.
• Provide technical leadership and mentorship to a distributed team.
• Manage priorities and end-to-end deliverables powering Google's AI hyper-computers and data centers.
• Drive large-scale programs to maximize production health, reliability, and scalability.

Skills

C++ programming

Software testing

Large-scale infra

Software architecture

GPU programming

ML infrastructure

AI platforms

ML optimization

Education

Bachelor's degree

Master's degree

Job description

Socket.dev is seeking a software engineer with deep expertise in C++, GPU programming, and distributed systems to help scale enterprise AI infrastructure. The role emphasizes leadership, architecture, and collaboration with hardware teams to deliver reliable accelerator software across TPUs and GPUs.

The ideal candidate has multi-year experience in large-scale systems, machine learning infrastructure, and cross-functional programs, with a record of technical leadership and project direction.