TPU ML Onboarding & Optimization Tech Lead

Google; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Google Singapore is seeking a Senior Staff Tech Lead Manager to own onboarding and optimization for TPU-based training and serving workloads. You will set technical direction, architect frameworks, and lead multiple teams across sites to deliver scalable ML pipelines.

You will collaborate with ML research, performance, and tooling groups, and partner with customers to onboard workloads to production while driving latency and throughput improvements on large-scale infrastructure.

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 (e.g. Megatron-LM, DeepSpeed) and inference techniques (e.g. TensorRT-LLM, vLLM, SGLang).
• 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

Software development

ML design leadership

Stakeholder influence

People management

Education

Bachelor’s degree or equivalent

Master’s degree or PhD preferred

Tools

Megatron-LM

DeepSpeed

TensorRT-LLM

vLLM

SGLang

Job description

Google Singapore is seeking a Senior Staff Tech Lead Manager to own onboarding and optimization for TPU-based training and serving workloads. You will set technical direction, architect frameworks, and lead multiple teams across sites to deliver scalable ML pipelines.

You will collaborate with ML research, performance, and tooling groups, and partner with customers to onboard workloads to production while driving latency and throughput improvements on large-scale infrastructure.