Senior Software Engineer, Full Stack, Search, Discover Exploration

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in C++, production systems, and machine learning.
• 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 full stack development across back-end, such as Java, Python, GO, or C++ codebases, and front-end experience, including JavaScript or TypeScript, HTML, CSS, or equivalent.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 1 year of experience in a technical leadership role.
• Experience implementing and shipping deep learning models (e.g., two-tower or other types of deep neural networks) for retrieval or ranking in a major recommendation product.
• Experience developing accessible technologies and with algorithms designed to improve content and solve the exploration/exploitation tradeoff.
• Experience with the infrastructure that powers modern search and recommendation systems

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.

In this role, you will take a holistic, end-to-end view of the system to identify optimization opportunities and apply state-of-the-art machine learning—including LLMs like Gemini—to significantly broaden content for users. Your work will span a wide variety of content types, including videos, UGC from social platforms, web articles, and first-party content (Shopping, RealWorld Journeys).

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

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 benefits at Google.

Responsibilities

• Participate in or lead design reviews with peers and stakeholders to decide amongst available technologies.
• Design, build, and ship production machine learning models and exploration algorithms (e.g., contextual bandits) for retrieval, scoring, and ranking in Discover's highly-scalable serving system.
• Work across the full technical stack in C++ and Python. This includes prototyping and implementing machine-learning models, building real-time data pipelines, integrating with core Search infrastructure, leveraging serving systems, and running experiments at scale.
• Direct the full project lifecycle by analyzing results, iterating based on metrics and product feedback, and applying deep user-centric judgment to define what makes content truly helpful for users.
• Thrive in ambiguity by navigating speculative problem spaces and translating high-level product goals into concrete technical solutions.