Software Engineer, Google Research

Minimum qualifications:

• Bachelor's degree in Computer Science or equivalent practical experience.
• 5 years of experience with distributed Machine Learning and Machine Learning infrastructure, distributed systems and with Machine Learning algorithms at scale.
• Experience with applied computer vision or Machine Learning research and development, and supporting infrastructure.
• Experience with Large Language Models (LLMs).

Preferred qualifications:

• PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.
• Experience in Machine Learning, or research achievements in the field of AI (e.g., product ownership, publications).

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

This is a software engineering role for creating RoboRating capabilities with existing Large Language Models (LLMs) that produce reliable, reproducible ratings for real-world tasks. These tasks can include search result rating, search quality rating, Ad rating, Advertiser intent rating, Ad quality rating, and also rating visuals, presentation and aesthetics. These ratings are designed to be personalized to some target users. The responsibilities include being current research on user-modeling, user behavior simulation, and designing simulation systems that match or beat on real world user journeys. Once the user simulation is accurate, the responsibility also includes rating the journey produced against a slate of evaluation criteria, like helpfulness to the user, suitability to the task, keeping the user persona in mind.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Responsibilities

• Understand current academic literature on LLM models' capabilities, and research on autorating (through LLMs, and otherwise), user simulation, and evaluation.
• Understand the current system, and the needs for rating and simulation tasks in the Google Ads context.
• Build and deliver functioning systems to Google Ads, with the goal to improve the next generation of advertising system, for the user, the advertiser, while keeping in mind Google's serving constraints and systems.