Software Engineer III, Machine Learning, YouTube Ads

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in C++ or Python, or 1 year of experience with an advanced degree.
• 1 year of experience with one or more of the following: generative AI, large language modeling, reinforcement learning (e.g., sequential decision making), recommendation systems, or specialization in another ML field.
• 1 year of experience in ML (e.g., model deployment, model
  evaluation, optimization, data processing, debugging).
  

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related technical field.
• 2 years of experience with data structures or algorithms.
• Experience developing accessible technologies.
  

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.

The YouTube Ads ML team is a modeling focused product team where we apply SOTA Machine learning techniques for making ad recommendations on YouTube with the following goals to deliver an ad experience which is useful, relevant and personalized to the user's interests and preferences, fulfill advertiser goals by identifying high value users who will purchase advertiser’s products and services, and generate ad business to keep the content free for YouTube users and support the creator ecosystem.

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.

Responsibilities

• Write product or system development code. 
  
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  
• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
  
• Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
  
• Build end-to-end machine learning systems on large-scale data.