Software Engineer III, Machine Learning, Shopping Targeting

Google • Mountain View, CA

6h

About The Position

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.

Shopping Search Ads offers a seamless experience for users to browse, compare, and buy products directly from search results. The Shopping Targeting team owns and operates the shopping targeting stack, which determines what ad candidates to participate in auction.

As a Software Engineer, you will innovate within our high-throughput, low-latency targeting stack and pioneer the integration of GenAI models (like Gemini) for Shopping Ads retrieval in new AI experiences. You will own projects end-to-end, shaping the future of how users discover and buy products on Google, with a direct impact on business and user experience at scale. This is a unique opportunity to combine ML expertise with real-time, large-scale challenges in the exciting evolution of AI-driven search advertising.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.

The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google [https://careers.google.com/benefits/].

Requirements

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience programming in Python or C++.
• 1 year of experience with one or more of the following: information retrieval, reinforcement learning (e.g., sequential decision making), ML Modeling, or specialization in another ML field.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Nice To Haves

• Master's degree or PhD in Computer Science or related technical fields.
• 2 years of experience with data structures and algorithms.
• Experience with LLMs and Generative AI modeling (e.g., prompt engineering, reinforcement learning, model fine tuning).
• Experience developing accessible technologies.

Responsibilities

• Develop and refine machine learning techniques to improve Shopping Ads targeting performance and quality.
• Leverage LLMs like Gemini to explore novel targeting solutions for new AI interfaces (e.g. AI Mode).
• Own projects end-to-end such as ideation, design, analysis, and implementation.
• Manage the full ML model lifecycle such as training, evaluation, deployment, and identification of improvement opportunities (e.g., new architectures, data, signals, evaluation methods).

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level