Software Engineer III Machine Learning, YouTube Ads

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 2 years of experience with software development in Python and C++, or 1 year of experience with an advanced degree.
  
  
• 1 year of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), machine learning infrastructure, or specialization in another machine learning field.
• 1 year of experience with Machine learning infrastructure (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.
• Experience with Machine Learning, TensorFlow, Flume.
• Experience developing accessible technologies.

Familiarity with parallel/distributed computing (NoSQL, MapReduce, etc).

Experience with Machine Learning Infrastructure.

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.

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 $136,000-$200,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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).
  
• Implement solutions in one or more specialized machine learning areas, utilize machine learning infrastructure, and contribute to model optimization and data processing.
  
• Design, build, and optimize models trained on massive datasets (billions of YouTube queries) using tools like TensorFlow, Adbrain, Smartass, and Tensor Processing Units(TPUs) to improve YouTube's recommendation system and ad products.
• Improve model efficiency, reliability, and freshness. Implement automation for model development and maintenance, perform data analysis, experimentation and research alongside novel deep learning techniques.