Staff Software Engineer, Shopping Ads, Predicted CTR Modeling
at Alphabet
Posted 5 hours ago
No clicks
- Compensation
- $197,000 – $291,000 USD
- City
- Not specified
- Country
- United States
Currency: $ (USD)
As a Staff Software Engineer on Google's Shopping Ads Predicted CTR Modeling team, you will set technical direction for model architectures and hyper-parameters to improve CTR predictions. You will train models on large-scale data using TPUs, implement training and serving components in Python and C++, and collaborate with ML researchers and ML infrastructure teams to optimize training and inference efficiency. This role directly impacts the Shopping Ads experience on Google.com and related surfaces, requiring deep ML expertise and ownership of modeling outcomes.
Staff Software Engineer, Shopping Ads, Predicted CTR Modeling
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Minimum qualifications:
- Bachelor's degree in Computer Science or related technical field, or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), or applied ML (e.g., deep learning, natural language processing).
- Experience with TensorFlow, TFX, TorchX, JAX, PyTorch, YDF, Vertex AI or other ML modeling and pipeline technologies.
- Experience using Python libraries and frameworks.
Preferred qualifications:
- Bachelor's degree in Computer Science or related fields (e.g., Math, Physics, Engineering), with an emphasis on Machine Learning.
- Experience building and deploying large scale machine learning models for recommendation applications.
- Knowledge and experience with modern Machine Learning techniques including deep learning, transformers, and model optimization.
- Knowledge of statistics and experiment design.
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 Shopping Ads aims to make Google the best place for users’ purchase, price-comparison, and shopping informational needs. The Shopping Auction Predicted Click-Through Rate (pCTR) Models Team plays a critical role in driving this fast-growing business! Our features and models impact the shopping commercial unit on Google.com, often the top of the page for a shopping-related query such as [buy nikon camera]. We also impact commercial units on image search [query: prom dress] and shopping mode [query: mountain bike], where users may have a more visual or in-depth shopping journey.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.
Responsibilities
- Set technical direction and explore improvements to model architectures, components, and hyper-parameters in order to continuously improve model accuracy.
- Train machine learning models, making use of Tensor Processing Units (TPUs) dedicated to the team.
- Implement code and tests for model training (Python, C++) and backend code and tests for model serving (in C++) as needed.
- Collaborate with Machine Learning (ML) researchers on the methods of improving models that train on enormous amounts of data.
- Collaborate with ML infrastructure teams to optimize training and inference efficiency.
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