Staff ML Engineer — Auto-Bidding for Search Ads

Google; New York (NY); On-site; USD 207,000 - 300,000; Full time

Job summary

Google is seeking a software engineer with deep expertise in AI, ML and large language models to advance our auto-bidding research and production systems. You will influence model design, experiment protocols, and scalable infrastructure across the lifecycle from concept to deployment.

The role requires hands-on work with deep learning, Python and C++, plus experience delivering production ML systems and optimizing performance in high-scale environments, including ad-tech applications.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• Experience with deep learning, transformers, or large language models (LLMs).
• Experience with machine learning model training and inference performance, and programming in Python or C++.

Responsibilities

• Innovate and iterate on machine learning model design to improve quality, stability, and efficiency across the entire model lifecycle from concept to deployment.
• Solve complex machine learning problems by designing, running, and analyzing experiments using analytical and statistical methods.
• Contribute to code health, automation, and alerting systems to ensure model stability and performance.
• Participate in on-call rotations for model health and collaborate with team members to support critical systems.

Skills

Deep learning

Transformers/LLMs

Python

C++

Model training and inference

Education

Bachelor’s degree

Master’s degree or PhD (preferred)

Tools

TensorFlow

Keras

TFX

Job description

Google is seeking a software engineer with deep expertise in AI, ML and large language models to advance our auto-bidding research and production systems. You will influence model design, experiment protocols, and scalable infrastructure across the lifecycle from concept to deployment.

The role requires hands-on work with deep learning, Python and C++, plus experience delivering production ML systems and optimizing performance in high-scale environments, including ad-tech applications.