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Software Engineer III, Knowledge and Information - Job Opportunity at Google

Zürich, Switzerland

Full-time

Mid-level

Posted: June 9, 2025

On-site

CHF 140,000 - 180,000 per year (approximately USD 155,000 - 200,000). This estimate reflects the premium compensation typical for Google's Zürich office, which competes with both local financial services firms and international tech companies for top AI talent. The Level III designation and generative AI focus command higher compensation due to the specialized skill set and current market demand for these competencies.

Benefits

Equal opportunity workplace with comprehensive diversity and inclusion policies that create a more innovative and collaborative work environment

Accommodation support for applicants with disabilities demonstrating commitment to accessibility and inclusive hiring practices

Opportunity to work on products that impact billions of users globally, providing unparalleled scale and visibility for professional portfolio

Access to cutting-edge AI and machine learning infrastructure and resources that are industry-leading

Cross-functional collaboration opportunities with world-class engineers and researchers in AI, search, and distributed systems

Key Responsibilities

Lead technical mentorship and knowledge transfer initiatives by training team members on client support methodologies, establishing you as a technical leader and subject matter expert within the organization

Drive strategic client engagement processes that directly impact user experience and business outcomes, positioning you at the intersection of technical innovation and customer success

Architect and implement advanced machine learning models, loss functions, and reinforcement learning algorithms that power Google Search's next-generation capabilities, contributing to systems that serve billions of queries daily

Collaborate on cross-functional projects that span information retrieval, distributed computing, and large-scale system design, building expertise across Google's entire technology stack

Contribute to the reimagining of search technology through the development of generative AI solutions that will define the future of information access and discovery

Requirements

Education

Bachelor's degree or equivalent practical experience

Experience

2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree. 2 years of experience with data structures or algorithms. 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging). 1 year of experience with core Generative AI concepts (e.g., LLM, Multi-Modal, Large Vision Models) and with text, image, video, or audio generation

Required Skills

Software development in one or more programming languages Data structures and algorithms ML infrastructure including model deployment, model evaluation, data processing, and debugging Core Generative AI concepts including LLM, Multi-Modal, Large Vision Models Text, image, video, or audio generation Reinforcement Learning algorithms (preferred) Large Language Model-based embedding training and applications (preferred)

Sauge AI Market Intelligence

Industry Trends

The integration of generative AI into search and information retrieval systems represents one of the most significant technological shifts in the past decade, with companies racing to implement LLM-based solutions that can understand and generate human-like responses to complex queries. This trend is creating unprecedented demand for engineers who can bridge traditional search algorithms with modern AI capabilities, particularly those who understand both the technical implementation and the user experience implications of these systems. Reinforcement learning from human feedback (RLHF) has become a critical technique for improving AI model performance and safety, driving demand for engineers who can implement and optimize these feedback loops. The market is seeing increased investment in RLHF infrastructure and methodologies, making experience in this area extremely valuable for career advancement. Multi-modal AI systems that can process and generate text, images, video, and audio simultaneously are becoming the new standard for next-generation applications. Companies are prioritizing engineers who have hands-on experience with these complex systems, as they represent the future of human-computer interaction and content generation. The shift toward personalized and contextual AI experiences is driving the need for sophisticated embedding systems and retrieval-augmented generation (RAG) architectures. Engineers with experience in large-scale embedding training and application are finding themselves at the center of this transformation, particularly in roles that involve improving search relevance and recommendation systems.

Role Significance

Typical team structure involves 8-12 engineers across different levels, with Level III engineers often leading technical workstreams and mentoring 2-3 junior engineers. The role likely involves collaboration with cross-functional teams including product managers, UX researchers, and data scientists, requiring strong communication and project coordination skills.

This Level III position represents a mid-level individual contributor role with significant technical ownership and mentoring responsibilities. The role sits at a critical juncture where technical expertise meets strategic impact, requiring both deep AI/ML knowledge and the ability to guide other engineers. This level typically involves making architectural decisions that affect product direction and user experience for millions of users.

Key Projects

Development and optimization of large-scale neural retrieval systems that can understand complex user queries and return relevant results from Google's massive index Implementation of multi-modal search capabilities that allow users to search using combinations of text, images, and voice inputs Creation of personalized search experiences through advanced embedding techniques and user behavior modeling Integration of generative AI features that can provide direct answers and summaries rather than just links to relevant content Development of reinforcement learning systems that continuously improve search quality based on user feedback and engagement metrics

Success Factors

Technical depth in both traditional information retrieval methods and cutting-edge generative AI techniques is essential for bridging Google's existing search infrastructure with next-generation capabilities. Success requires understanding how to scale AI models to handle billions of queries while maintaining response speed and accuracy. Strong system design skills for building robust, scalable ML infrastructure that can handle the computational demands of large language models and multi-modal AI systems. This includes understanding distributed computing, model serving, and efficient data processing pipelines. Collaborative leadership abilities to effectively mentor team members and drive client engagements while maintaining technical excellence. The role requires balancing hands-on coding with strategic thinking and team development. Adaptability and continuous learning mindset given the rapid pace of AI advancement and Google's culture of innovation. Success depends on staying current with the latest research and being able to quickly prototype and evaluate new approaches. Product-focused engineering approach that considers user experience and business impact alongside technical sophistication. Understanding how AI improvements translate to better search experiences and user satisfaction is crucial.

Market Demand

Extremely High - The convergence of search technology and generative AI has created a seller's market for engineers with this specific skill combination, particularly at companies operating at Google's scale and technical sophistication.

Important Skills

Critical Skills

Generative AI and Large Language Model expertise is absolutely essential as Google integrates these technologies into its core search products. Understanding model architecture, training procedures, inference optimization, and practical deployment challenges directly impacts the company's competitive position in the AI-driven search landscape. This skill set is the foundation for contributing to Google's next-generation search capabilities. Machine Learning infrastructure and model deployment skills are crucial for translating AI research into production systems that serve billions of users. This includes understanding distributed training, model serving architectures, A/B testing frameworks for ML systems, and monitoring/debugging complex AI pipelines that operate at unprecedented scale. Data structures and algorithms proficiency remains fundamental for optimizing search performance and developing efficient AI systems. At Google's scale, algorithmic efficiency improvements can translate to significant computational cost savings and improved user experience, making this traditional computer science foundation critically important. Software development expertise across multiple programming languages provides the technical foundation for implementing complex AI systems and integrating them with existing search infrastructure. Strong coding skills are essential for both prototyping new approaches and building production-ready systems.

Beneficial Skills

Reinforcement Learning and RLHF experience is increasingly valuable as Google implements more sophisticated feedback mechanisms to improve search result quality and AI-generated content. Understanding these techniques positions engineers to work on the next evolution of search personalization and result optimization. Multi-modal AI capabilities spanning text, image, video, and audio processing are becoming essential as search evolves beyond text queries to include visual search, voice search, and comprehensive content understanding. This skill set is crucial for developing the next generation of search interfaces. Large-scale distributed systems experience helps engineers understand the infrastructure challenges of deploying AI at Google's scale, including challenges around latency, throughput, and reliability that are unique to serving billions of users globally. Research and publication experience in AI/ML fields can accelerate career growth and provide opportunities to contribute to Google's research initiatives while building industry recognition and technical credibility.

Unique Aspects

This role sits at the intersection of Google's two most strategically important initiatives: maintaining search dominance and leading the generative AI revolution, providing exposure to both mature, large-scale systems and cutting-edge AI research applications.

The position offers direct involvement in reimagining search technology that will define how billions of people access information in the age of AI, representing a once-in-a-career opportunity to shape fundamental internet infrastructure.

Working on Google Search provides access to unique datasets, computational resources, and technical challenges that exist nowhere else in the industry, including the opportunity to work with proprietary large language models and multi-modal AI systems.

The role combines individual technical contribution with mentoring and client engagement responsibilities, providing a well-rounded experience that develops both technical depth and leadership skills essential for senior engineering roles.

Being based in Zürich offers the opportunity to work on global products while benefiting from European work culture, competitive compensation adjusted for local standards, and access to Google's international engineering community.

Career Growth

Progression to Senior Engineer typically occurs within 2-3 years with strong performance and demonstrated impact on user-facing features. Staff-level positions generally require 4-6 years of experience with significant technical contributions and industry recognition.

Potential Next Roles

Senior Software Engineer (Level IV/V) with expanded technical leadership responsibilities and ownership of larger system components Staff Engineer specializing in AI/ML infrastructure with company-wide impact on search and AI product development Technical Lead Manager combining people management with deep technical oversight of AI initiatives Principal Engineer focusing on next-generation search architecture and AI research application Product-focused roles such as Senior Product Manager for Search AI or Director of Search Engineering

Company Overview

Google

Google represents the global leader in search technology and information organization, with its search engine processing over 8.5 billion queries daily and serving as the primary gateway to information for billions of users worldwide. The company has successfully positioned itself at the forefront of the AI revolution, with substantial investments in large language models, multi-modal AI systems, and next-generation search capabilities that integrate generative AI directly into the search experience.

Google maintains an unassailable dominant position in the global search market with approximately 92% market share, while simultaneously leading innovation in AI and machine learning applications. The company's combination of massive data resources, computational infrastructure, and top-tier AI research talent creates a unique environment where cutting-edge research can be immediately applied to products serving billions of users.

Google's Zürich office serves as a critical European engineering hub and the company's largest engineering office outside the United States, housing over 5,000 employees working on core Google products including Search, YouTube, and AI initiatives. The office benefits from Switzerland's strong technical talent pool, favorable business environment, and strategic location for serving European markets while maintaining close collaboration with Mountain View headquarters.

Google's engineering culture emphasizes innovation, technical excellence, and data-driven decision making, with a strong focus on 20% time for exploration and cross-functional collaboration. The Zürich office maintains Google's characteristic flat organizational structure and open communication style while benefiting from European work-life balance sensibilities and the city's high quality of life.

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Software Engineer III, Knowledge and Information - Job Opportunity at Google

Benefits

Key Responsibilities

Requirements

Education

Experience

Required Skills

Sauge AI Market Intelligence

Industry Trends

Role Significance

Key Projects

Success Factors

Market Demand

Important Skills

Critical Skills

Beneficial Skills

Unique Aspects

Career Growth

Potential Next Roles

Company Overview

Google

Data Sources & Analysis Information

Job Listings Data

Sauge AI Market Intelligence