
Organizing the world's information and making it universally accessible.
Senior Software Engineer, Generative AI, LearnX
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 be a key contributor in developing Artificial Intelligence (AI)-motivated capabilities and features for learning products in Search Web and Lens. You will work in close collaboration with product, surface specific engineering and research teams. You will include creating a training dataset, fine-tuning, prompt optimization, evaluating model performance, detecting headroom, launching AI-motivated features and ensuring surface specific quality. You will be involved in designing tech solutions to issues, collaborating with stakeholders and driving delivery.
Responsibilities
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Drive design and implementation of AI features for education in Search Web and Lens.
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Analyze model performance, diagnose loss patterns, and ensure high quality of AI educational user journeys.
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Drive cross-team collaboration with product and surface engineering teams, participate in product initiation, plan timelines and ensure engineering delivery.
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Propose improvements of the team's processes, practices and ensure technical excellence.
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Demonstrate growth mindset, self-reflection, mentor other engineers on the team and share expertise in Artificial Intelligence/Machine Learning (AI/ML) best practices and model development.
Minimum qualifications
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Bachelor’s degree or equivalent practical experience.
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5 years of experience with software development in one or more programming languages.
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3 years of experience in AI/ML related engineering.
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1 year of experience in a technical leadership role with software design and architecture; leading tech design and driving team execution.
Preferred qualifications
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Master's degree or PhD in Computer Science or a related technical field.
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5 years of experience with data structures and algorithms.
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Experience in machine learning, building/training AI models, backend data infrastructure or data engineering.
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Experience in developing, deploying, managing, and maintaining machine learning models and infrastructure.
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Understanding of data engineering practices and principles (e.g., tools, processes, pipelines etc.).
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Ability to collaborate with other people, teams across the world and communicate cross-functionally.
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About Google

Google specializes in internet-related services and products, including search, advertising, and software.
10,001+
Employees
Mountain View
Headquarters
$1,700B
Valuation
Reviews
10 reviews
4.5
10 reviews
Work-life balance
3.2
Compensation
4.3
Culture
4.1
Career
4.2
Management
3.8
82%
Recommend to a friend
Pros
Great benefits and perks
Innovative and interesting work
Career development and learning opportunities
Cons
High pressure and expectations
Long hours and heavy workload
Fast-paced and overwhelming environment
Salary Ranges
57,503 data points
Mid/L4
Mid/L4 · Accessibility Analyst
1 reports
$214,500
total per year
Base
$165,000
Stock
-
Bonus
-
$214,500
$214,500
Interview experience
9 interviews
Difficulty
3.4
/ 5
Duration
14-28 weeks
Offer rate
44%
Experience
Positive 0%
Neutral 56%
Negative 44%
Interview process
1
Application Review
2
Online Assessment/Technical Screen
3
Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
Common questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Product Sense
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