Staff Software Engineer – ML Systems & TPU Performance

Google LLC; Mountain View (CA); Sur place; USD 207 000 - 300 000; Plein temps

Avantages offerts par ce poste

Equity

Bonus target

Benefits

Résumé du poste

Google LLC in Mountain View seeks a senior software engineer to advance high-performance ML workloads and TPU silicon integration. You will craft architectures, validate models, and drive performance improvements across distributed systems at scale.

You will mentor teams, collaborate with hardware, compilers, and AI research groups to deliver pre-pilot benchmarks and robust reference implementations using PyTorch/JAX/Pallas.

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.

Responsabilités

• Deliver certified performance benchmarks six months pre-pilot as primary targets for pricing, capacity planning, and kernel optimization.
• Adapt emerging open-source models into TPU-native paired variants, including sparsity optimization.
• Ingest and optimize multi-turn agentic workflows, reasoning loops, and prompt and decode topologies prior to silicon tape-out.
• Author robust reference implementations using PyTorch, JAX, or Pallas that meet pre-pilot goals under compiler constraints.
• Partner with director and executive technical leadership across TPU Hardware, XLA Compiler, and Cloud AI Systems to steer multi-year roadmaps.

Connaissances

C++

Python

Testing

System design

Performance analysis

Formation

Bachelor's degree or equivalent practical experience

Outils

MLIR

XLA

PyTorch

PyTorch/XLA

CUDA

Triton

Pallas

Description du poste

Google LLC in Mountain View seeks a senior software engineer to advance high-performance ML workloads and TPU silicon integration. You will craft architectures, validate models, and drive performance improvements across distributed systems at scale.

You will mentor teams, collaborate with hardware, compilers, and AI research groups to deliver pre-pilot benchmarks and robust reference implementations using PyTorch/JAX/Pallas.