Software Engineer, AI/ML, YouTube Ads
Job Overview
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Job Description
About the Software Engineer, AI/ML, YouTube Ads Role at Google
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, AI/ML, YouTube Ads, 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.
The US base salary range for this full-time position is $166,000-$244,000 + 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.
Minimum Qualifications
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 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 ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
Preferred Qualifications
- Master's degree or PhD in Computer Science or a related technical field.
- 5 years of experience with data structures and algorithms.
- 3 years of experience working with Large Language Models (LLMs).
- 1 year of experience in a technical leadership role.
- Experience developing accessible technologies.
Responsibilities
- Write and test 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.
- Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.
Key Skills/Competency
- Software Development
- Machine Learning (ML)
- Artificial Intelligence (AI)
- ML Infrastructure
- Large Language Models (LLMs)
- Data Structures
- Algorithms
- Distributed Computing
- System Design
- Code Review
How to Get Hired at Google
- Research Google's AI/ML initiatives: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor, especially focusing on AI/ML and advertising.
- Tailor your resume for Google: Highlight experience with large-scale systems, machine learning infrastructure, AI model development, and relevant programming languages like Python or C++.
- Prepare for technical interviews: Focus on data structures, algorithms, system design, and specialized ML concepts relevant to the Software Engineer, AI/ML, YouTube Ads role.
- Showcase leadership and versatility: During interviews, articulate instances where you've taken technical leadership, solved complex problems, and adapted to new challenges effectively.
- Demonstrate problem-solving skills: Be ready to discuss your approach to debugging, issue resolution, and contributing to documentation and product improvement.
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