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Open nowFirst seen 2 hours ago

Software Engineer III, AI/ML, Search Ads

Google3,566 open roles

Where
Mountain View, CA, USA
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Your applicationOpen nowSoftware Engineer III, AI/ML, Search AdsGoogle · Mountain View, CA, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Google's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

9.5% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 28 days.

Share of postings closed within
  1. 1.8%1 day
  2. 3.9%3 days
  3. 9.5%7 days
  4. 15.5%14 days
  5. 33.9%30 days
This job: first seen 2 hours ago

Google median: 28 days open

The posting

In most instances, this position requires in-person interviews as part of the hiring process.

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience with software development in C++, Java, and using LLM coding tools.
  • 1 year of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
  • 1 year of experience with one or more of the following: Gen AI/LLM tools, reinforcement learning (e.g., sequential decision making), Machine Learning (ML) infrastructure, or specialization in another ML field.

Preferred qualifications:

  • Master's degree or PhD in Computer Science, or in a related technical field.
  • Experience with the latest research and trends in AI/ML.
  • Experience designing and building scalable fullstack systems and self-service platforms.
  • Knowledge of search/Ads stack.
  • Excellent analytical thinking and data analysis skills.

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 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: $147000 - $210000 (USD) + 15% bonus target + equity + 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).
  • Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  • Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
  • Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
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