Staff ML Systems Co-Design Engineer

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

Job summary

Google is seeking software engineers to drive TPU performance and open-source ML model strategy. You will work across hardware, compiler, and cloud AI teams to map models to TPU architecture and accelerate real-world workloads.

You will design, test, and validate high-performance software, influence multi-year roadmaps, and deliver scalable open-source ML solutions aligned with Silicon timelines.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of experience programming in C++ or Python.
• 5 years of experience testing, and launching software products.
• 5 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
• 3 years of experience with software design and architecture.

Responsibilities

• Deliver certified performance ladders 6 months pre-pilot as the canonical goals for pricing, capacity planning, and XLA/kernel optimization.
• Adapt emerging open-source models into TPU-native twin variants to prove asymmetric TPU superiority.
• Ingest and optimize multi-turn agentic workflows, reasoning loops, and prompt/decode disaggregated topologies ahead of silicon.
• Author high-signal reference implementations (PyTorch/JAX/Pallas) that guarantee reachability of pre-pilot goals under real-world compiler constraints.
• Partner with L7-L9 leaders across TPU Hardware, XLA Compiler, and Cloud AI Systems to steer multi-year roadmaps.

Skills

C++

Python

Software testing

Performance analysis

Large-scale systems

Debugging

Software design

Education

Bachelor's degree or equivalent

Tools

MLIR

XLA

PyTorch

CUDA

Job description

Google is seeking software engineers to drive TPU performance and open-source ML model strategy. You will work across hardware, compiler, and cloud AI teams to map models to TPU architecture and accelerate real-world workloads.

You will design, test, and validate high-performance software, influence multi-year roadmaps, and deliver scalable open-source ML solutions aligned with Silicon timelines.