Staff ML Systems Co-Design Engineer — TPU & Open-Source ML

Google; Kirkland (WA); On-site; USD 207,000 - 300,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid time off

Maternity leave

Baby bonding leave

Holidays

Job summary

Google is seeking a software engineer to join the MARC team, a co-design engine at the frontier of ML workloads and TPU silicon roadmaps. You will define strategies to map open-source ML models to TPU performance and drive scalable analysis for model families.

You will collaborate with TPU Hardware, XLA, and Cloud AI teams to shape multi-year roadmaps and ensure open-source models reach TPU performance targets at launch.

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 definitive goals for pricing, capacity planning, and XLA/kernel optimization.
• Adapt emerging open-source models into TPU-native paired variants (e.g., sparsity optimization) 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 director and executive technical leadership across TPU Hardware, XLA Compiler, and Cloud AI Systems to steer multi-year roadmaps.

Skills

C++

Python

Software testing

Performance analysis

System design

Education

Bachelor's degree or equivalent

Tools

PyTorch

JAX

Pallas

CUDA

MLIR/XLA/HLO

Job description

Google is seeking a software engineer to join the MARC team, a co-design engine at the frontier of ML workloads and TPU silicon roadmaps. You will define strategies to map open-source ML models to TPU performance and drive scalable analysis for model families.

You will collaborate with TPU Hardware, XLA, and Cloud AI teams to shape multi-year roadmaps and ensure open-source models reach TPU performance targets at launch.