Description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 5 years of experience with software development in C or Python.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
  
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

• Master's degree or PhD in Computer Science, Mathematics, or a related science or technical field.
• 
  
  5 years of experience with data structures and algorithms.
  
  
• 1 year of experience in a technical leadership role.
  
• Experience with ads bidding, mathematical optimization, or feedback loops.

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 Proxybidder team's mission is to build exceptional bidding modeling, optimization algorithms and platforms for Search and Shopping Ads. The team is focused on building and growing adoption of the next generation of bidding technology and products to drive value to all types of advertisers. This includes the Search and Shopping Bidding systems (ML, optimization systems, new bid strategies and algorithms, data systems, and platforms) and launching both advertiser-facing bidding products and backend improvements. The team works closely with almost all parts of the ads stack such as budgeting, targeting, creatives, ads platform, auctions, enabling us to take up research projects that make the foundation for the next generation of Ad Tech.

The team builds bidding algorithms, optimization techniques, and platforms and interacts with ML systems.

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 $174,000-$252,000 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 and test 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).
  
• Work on improving and simplifying models through advanced machine learning techniques.
  
• Innovate and iterate on machine learning model design, improving quality, stability, and efficiency across the entire model lifecycle-from concept to deployment.
  
• Solve complex machine learning related problems by designing, running, and analyzing experiments using analytical and statistical methods.
  

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Requisition #: 98949083774755526

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