Software Engineer, Machine Learning

Google•New York, NY

17h•$207,000 - $300,000

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. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. In this role, you will build and maintain machine learning models using Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict user interactions on Search Advertisements. These models are a key component in setting advertisers' bids, with the goal of improving both satisfaction and Return on Investment for Search Advertisements advertisers using Automated-bidding products. By optimizing towards advertisers' objectives, Automated-bidding products drive Google's global Advertisements business, which serves billions of users. 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.

Requirements

Bachelor’s degree or equivalent practical experience.
8 years of experience programming in Python or C++.
5 years of experience managing ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
5 years of experience building and deploying recommendation systems models (e.g., retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
5 years of experience testing, and launching software products.
3 years of experience with software design and architecture.

Nice To Haves

Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
8 years of experience with data structures and algorithms.
Experience productionizing ML systems.
Experience with Advertising Machine Learning, Lego Machine Learning, Keras, or TensorFlow Extended.
Experience in machine learning, statistics, applied mathematics, or operations research within an industrial or academic setting.
Ability to write high-quality and low-latency code and models capable of training and serving on every query at scale.

Responsibilities

Learn the bidding ML models that drive billions in advertisement business across Google Advertisements.
Work on improving and simplifying models through advanced ML techniques.
Innovate and iterate on ML model design, improving quality, stability, and efficiency across the entire model lifecycle from concept to deployment.
Solve ML related problems by designing, running, and analyzing experiments using analytical and statistical methods.
Engage in the full ML model lifecycle, from design and training to deployment and serving models in production at the scale of billions of Search Advertisements.
Train models on Tensor Processing Units, utilizing libraries such as Keras and TensorFlow, with plans to migrate to Just-In-Time Compilation in the future.