Senior Staff Software Engineer, AI/ML, Google Cloud Compute Infrastructure
Confirmed on the employer's own hiring board on Nov 8, 2026. First seen by Alion on Nov 4, 2026. Google scores B on the Alion truth index.
About the job
In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.
In this role, you will bridge the gap between model architecture innovation and next-generation hardware design. Operating at the intersection of cutting-edge AI research and infrastructure engineering, you will define the long-term strategic goal and architectural roadmap for the future machine learning training and serving capabilities. You will lead the integration of foundational ML research with advanced architectures to deliver industry-defining, high-performance, and power-efficient accelerators.
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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Lead the technical goal, roadmap, and software architecture for hardware/software co-design, optimizing large-scale ML inference and training systems.
- Design, develop, and oversee simulation frameworks, cycle-accurate performance models, and system analysis tools to evaluate architectural trade-offs.
- Architect scalable software solutions, kernels, and execution pipelines that bridge frontier ML model architectures with next-generation custom silicon accelerators.
- Drive system-level performance optimization across distributed ML infrastructure, balancing compute, memory bandwidth, and inter-chip networking requirements.
- Provide technical leadership and mentorship across hardware, compiler, and ML research teams to align on goals and transition co-design innovations into production.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, a related technical field, or equivalent practical experience.
- 8 years of experience in software development, systems engineering, or hardware/software co-design.
- 5 years of experience with machine learning systems, architecture, performance modeling, or simulation frameworks.
- Experience with ML frameworks (e.g., JAX, PyTorch, TensorFlow) and large language model (LLM) inference or training workloads.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 5 years of experience in a technical leadership role leading project teams and setting technical direction
- Experience driving multi-generational hardware/software co-design or performance optimizations for massive-scale ML inference and training.
- Experience with accelerator programming, ML compilers, and deep learning execution models.
- Experience analyzing system-level performance across compute, memory, interconnects, and packaging trajectories.