Senior ML Engineer: TPU Training & Inference Optimizer

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

Job summary

Google Singapore seeks a Senior Software Engineer specializing in Machine Learning and TPU workload optimization. You will lead onboarding for training and inference pipelines on the latest TPU NPIs, architecting end-to-end workflows, and collaborating with research and performance teams to deliver scalable solutions.

The role requires hands-on development, strong problem solving, and mentoring of junior team members, with in-person interviews often part of the process.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages.
• 2 years of experience leading ML design and optimizing ML infrastructure.
• 1 year of experience with training and inference techniques.

Responsibilities

• Act as ML software engineer leading onboarding for stable stack and optimization for training and serving solutions on the latest TPU NPIs.
• Architect training/inference onboarding and optimization frameworks for customer needs.
• Collaborate with ML research, ML performance, model optimization tooling, and other optimization teams.
• Ship stable stack to customers for training/inference optimization on TPU.
• Prepare and optimize very large reference models with SOTA single-host and multi-host inference at large scale.

Skills

Software development

ML design

Mentoring

Problem solving

Leadership

Education

Bachelor’s degree or equivalent

Master’s degree or PhD (preferred)

Tools

Megatron-LM

DeepSpeed

TensorRT-LLM

vLLM

SGLang

Job description

Google Singapore seeks a Senior Software Engineer specializing in Machine Learning and TPU workload optimization. You will lead onboarding for training and inference pipelines on the latest TPU NPIs, architecting end-to-end workflows, and collaborating with research and performance teams to deliver scalable solutions.

The role requires hands-on development, strong problem solving, and mentoring of junior team members, with in-person interviews often part of the process.