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.
Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.
Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].
modeling.
(training and serving). Prototype new infrastructure, automation, workflows, and tooling.
reliability, usability of infrastructure.
systems that take advantage of and integrate seamlessly across our ecosystem. Engage with modeling teams to explore pain points and novel use cases.
vs longer-term stability.
Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
multi-modal, large vision models) or with GenAI-related concepts (e.g., language modeling, computer vision).
technical field.
and setting technical direction.
cross-functional or cross-business projects.
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checked 3 days agoSource: the company job board. We last checked this job 7 days ago. We first saw it 7 days ago. A job on a job board does not always mean that the team interviews people now.
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This base pay is below the median for staff ML and AI engineering jobs in New York.
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