Senior Software Engineer, Machine Learning, Search
Job Summary
Job Description
- 5+ years C++ coding
- 3+ years building recommendation systems
- 3+ years ML infrastructure experience
- Experience with model deployment and evaluation
- Experience developing accessible technologies
- 3+ years ML or Quality experience in Recommendation Systems
- Proven track record of driving quality projects
- bonus
- equity
- benefits
4 skills required
Job Details
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of coding experience in C++.
- 3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production; and experience building architecture.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
Preferred qualifications:
- Experience developing accessible technologies.
- 3 years of ML or Quality experience working on Recommendation Systems.
- Ability to track record of driving quality projects from design to implementation to eventual launch.
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.
Discover is Google's Personalized Interest Feed, and helps 500M+ users across 100+ countries every day to get updates on their interests and explore the world. As one of the fastest growing Google products, Discover is the Home of Search, where users can get information to what they may be interested in and be inspired without even having to type a Search query - that's why we're known as the Proactive organization.
The mission of the Discover Personalization team is to help people feel positively inspired, connected and informed about the world around them. We deliver the pulse of the Internet that matters to you, right at the Home of Google Search.
In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.
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 .
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).
- Design and implement recommendation systems models across different domains, leverage Machine Learning (ML) infrastructure, and contribute to architecture design.
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