Staff Software Engineer, ML Systems, 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

Minimum 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 experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture.
  
• 3 years of experience with software design and architecture.
• Experience with GPU programming, machine learning infrastructure, AI platform, machine learning optimization, and systems infrastructure.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 8 years of experience with data structures and algorithms.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience with GPU/TPU Kernel Development, XLA, JAX/PyTorch, and ML Optimizations.

About the job:

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

In this role, you will guide the scaling of Google’s most critical AI infrastructure. You will build and direct the team ensuring the production health, rapid capacity turn-up, and flawless stability of our global Graphics Processing Unit (GPU) Superpods. You will serve as the ultimate authority on complex Hardware/Software (HW/SW) interactions, directly accelerating next-gen architectures (like GB300) from New Product Introduction (NPI) to General Availability.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Define the architecture and long-term technical roadmap for Google’s accelerator software stacks, focusing on distributed systems, Linux OS, networking, power management, and hardware interfaces.
• Collaborate with HW engineering to design, test, deploy, and debug low-level software across the stack—including firmware, kernel drivers, simulators, and cluster configurations for next-gen TPUs, GPUs, and custom accelerators.
• Provide technical leadership, mentorship, and coaching to a distributed team while fostering cross-functional alignment on goals, timelines, and program outcomes.
• Manage priorities and end-to-end deliverables to power Google's AI hyper-computers and data centers, supporting internal workloads and Google Cloud offerings.
• Drive large-scale technical programs to maximize production health, reliability, and scalability of distributed accelerator systems in partnership with multiple teams.