Software Engineer II, Google Search, Machine Learning
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MINIMUM QUALIFICATIONS:
- Bachelor’s degree or equivalent practical experience.
- 1 year of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
- 1 year of experience with data structures and algorithms.
- 1 year of experience implementing core Machine Learning (ML) concepts.
- Experience integrating generative AI tools or LLM interfaces into workflows.
PREFERRED QUALIFICATIONS:
- Solid understanding of Google's product development practices and common infrastructure systems.
- Skills in C++, algorithms, data analysis, data processing, and machine learning.
- Culture fit for the team and interest for the expertise area.
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.
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The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.
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RESPONSIBILITIES:
- Design, build, deploy, and iterate on machine learning models (including LLMs) to detect policy violations in shopping content.
- Develop and deploy scalable pipelines for high-quality training data curation, striving for gold-standard datasets and implementing robust frameworks for model evaluation and performance tracking.
- Leverage Large Language Models (LLMs) including auto-raters/agents to improve merchant experience, operations accuracy and efficiency. Deploy these for a multitude of use cases including handling escalations, reviewing user reports and merchant appeals and metrics generation and training data quality curation.
- Partner with product managers, policy specialists to identify and implement opportunities to support business growth and consumer protection.
- Collaborate with other Engineering teams (Shopping Infra, Compliance, Merchant Tools, Ads Safety) to integrate and support content safety measures.
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