Software Engineer, Real-Time Ad Personalization & Semantics

Socket.dev; Mountain View (CA); On-site; USD 147,000 - 210,000; Full time

Job summary

Google Ads is seeking software engineers to join our ads personalization and retrieval systems in Mountain View. You’ll work on end-to-end real-time models, content understanding, and scalable infrastructure to power a multi-billion dollar global business.

As a software engineer, you will write and test production code, participate in design reviews, and help shape choices across modeling and infrastructure teams.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience programming in Python or C++.
• 1 year of experience building and deploying recommendation systems models in production.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
• Experience with Large Language Model (LLMs), Multi-Modal, or Large Vision Modeling.

Responsibilities

• Write and test product or system development code.
• Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
• Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Build model and architectural components that provide effective attention for ads over user preferences and history.
• Design real-time infrastructure and representations of user viewed content.

Skills

Python

C++

ML infrastructure

Recommender systems

LLMs / Multimodal

Education

Bachelor’s degree or equivalent practical experience

Tools

Model deployment

Data processing

Job description

Google Ads is seeking software engineers to join our ads personalization and retrieval systems in Mountain View. You’ll work on end-to-end real-time models, content understanding, and scalable infrastructure to power a multi-billion dollar global business.

As a software engineer, you will write and test production code, participate in design reviews, and help shape choices across modeling and infrastructure teams.