Description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 5 years of experience with software development in C and 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 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), ML infrastructure, or specialization in another ML field.
  
• 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 or related technical field.
  
• 1 year of experience in a technical leadership role.
  
• Experience in data analysis for building and optimizing machine learning models, such as Neural Networks and Recommender Systems, specifically for large-scale Ads platforms.
• Passion to work in a fluid, high-visibility team.
• Passion to work in a cross-functional, collaborative environment that involves establishing mutually-beneficial relationships with research and product teams.

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.

In this role, you will work with deep models which serve large-scale traffic to Billions of YouTube users making real-time recommendations. The team is responsible for the entire end-to-end life-cycle from researching, prototyping, building, deploying and maintaining all these models. We develop new models ground up for new products and ad experiences on YouTube. We also work on designing new model architectures, feature engineering, metrics and evaluation strategies for increasing model performance.

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

• Work on identifying innovative solutions for various product, user, advertiser optimization problems.
• Build end-to-end machine learning systems on large-scale data (billions of YouTube queries).
• Research novel deep model architectures, read papers, implement and deploy them.
• Build models from inception to launch: Collect ground truth, exploratory models, feature engineering, deep model architectures, live experiments on YouTube users, tuning, metrics analysis.
• Data analysis both offline on model prediction accuracy, and online on live experiments on business critical metrics.

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Requisition #: 75570195413770950

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