Software Engineer II, AI/Machine Learning

Minimum

• 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 or algorithms.
• Experience integrating generative AI tools or LLM interfaces into workflows. Preferred

• Experience with Deep Learning, Information Retrieval, and Multimodal Machine Learning. 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. People shop on Google more than a billion times a day
• and the Commerce team is responsible for building the experiences that serve these users. The mission for Google Commerce is to be an essential part of the shopping journey for consumers
• from inspiration to to a simple and secure checkout experience
• and the best place for retailers/merchants to connect with consumers. We support and partner with the commerce ecosystem, from large retailers to small local merchants, to give them the tools, technology and scale to thrive in today’s digital world.
• Develop universal vector representations (embeddings) of products and related items, such as queries, brands, and categories.
• Build two-tower (dual-encoder) Transformer models that place queries and products in one vector space, utilizing a shared encoder that starts from a pretrained Gemini model.
• Train models on extensive datasets, utilizing user clicks and relevance labels distilled from a larger model.
• Execute training runs on shared Gemini infrastructure, encoding queries online with low latency while embedding and indexing product listings offline for fast nearest-neighbor vector search in the shopping search backend.
• Optimize multimodal Gemini embedding models that encode product thumbnails together with titles and descriptions to improve recall.

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