Software Engineer, TPU Host Networking
Google · Sunnyvale, CA
About this role
Google is hiring a mid-level Software Engineer based in Sunnyvale, CA. The posting calls out experience with PyTorch, Networking, Distributed Systems, Data Structures. Compensation is listed at $147,000–$211,000 per year.
- Role
- Software Engineer
- Function
- software engineering
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Sunnyvale, CA
- Posted
- May 12, 2026
More roles at Google
Job description
from Google careersGoogle'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.
Tensor Processing Units (TPU) are Google’s custom-built Application-Specific Integrated Circuits used to accelerate machine learning (ML) workloads. TPU are designed from the ground up leveraging Google’s deep experience and leadership in ML Learning.
As a team member in Tensor Processing Unit Host Networking, you will play a leading role in the design, development, testing, deployment, and debugging of the TPU networking stack, from hardware (Tensor Processing Unit, Network Interface Controller) all the way up to ML frameworks (JAX, PyTorch) to enable both large-scale training and low-latency inference applications.