Senior ML Systems Lead — TPU Onboarding & Optimization

Google; Singapore; On-site; SGD 180,000 - 300,000; Full time

Job summary

Google is seeking a Software Engineering Manager to lead ML design, optimization of training and inference infrastructure, and to guide teams across multiple sites. The role emphasizes hands-on technical leadership, setting direction for TPU/GPU-backed systems and scalable ML pipelines in cloud environments.

You will collaborate with ML research, performance, and tooling teams, onboard customers, and ship stable stacks for production workloads, with a focus on large-scale models and

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with software development.
• 5 years of experience leading ML design and optimizing ML infrastructure.
• 2 years of experience with state of the art training and inference techniques.
• 2 years of experience in a people management or team leadership role.

Responsibilities

• Act as a Senior Software Manager leading the onboarding for stable stack, and optimization for training and serving solutions on the latest TPU NPIs.
• Collaborate with the ML research, ML performance, model optimization tooling, and other optimization teams.
• Ship stable stack to our customers for training/inference optimization solutions on TPU.
• Prepare and optimize very large reference models, demonstrating state of the art single-host and multi-host inference solutions, especially at large-scale.
• Work closely with customers to onboard their workloads to production and optimization.

Skills

Bachelor’s degree or eq. pratical exp.

8 years software development

5 years ML design & infra

2 years training/inference techniques

2 years people management

Education

Bachelor's degree or eq. practical exp.

Master’s or PhD (preferred)

Tools

TPUs/GPUs

Megatron-LM

DeepSpeed

TensorRT-LLM

vLLM

Job description

Google is seeking a Software Engineering Manager to lead ML design, optimization of training and inference infrastructure, and to guide teams across multiple sites. The role emphasizes hands-on technical leadership, setting direction for TPU/GPU-backed systems and scalable ML pipelines in cloud environments.

You will collaborate with ML research, performance, and tooling teams, onboard customers, and ship stable stacks for production workloads, with a focus on large-scale models and