Software Engineer, AI/ML, Google Lens

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.

The Google Lens team is at the forefront of Google's multimodal evolution. We build the advanced algorithms, infrastructure, and evaluation frameworks that allow users to search and interact with the physical world using visual and conversational inputs. Within this space, our conversational quality initiatives focus on making interactions highly relevant, intuitive, and seamless for millions of users worldwide.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google .

Responsibilities

• Write product or system development code in languages such as Python, C++, or Java, focusing on building automated evaluation and telemetry pipelines for conversational AI systems.
• 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) within a highly cross-functional environment.
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback to ensure seamless onboarding and engineering alignment.
• 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 of conversational systems in production.
• Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.

Minimum qualifications

• Bachelor's degree in Computer Science, Software Engineering, Mathematics, a related technical field, or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages (e.g., Python, C++, Java), or 1 year of experience with an advanced degree.
• 1 year of experience with one or more of the following: Speech/audio, reinforcement learning, ML infrastructure, or specialization in another ML field.
• 1 year of experience with ML infrastructure or building automated data pipelines.
• 1 year of experience working with telemetry systems, event logging, or applying statistics within production software.

Preferred qualifications

• Master's degree or PhD in Computer Science or related technical fields.
• 2 years of experience with data structures or algorithms, system architecture, API design, and modular backend development.
• Experience with generative AI technologies, including prompt engineering, synthetic data generation, or implementing LLM-as-a-Judge automated grading systems.
• Experience developing accessible technologies to ensure user-facing conversational and multimodal interfaces are robust.
• Experience building experimentation infrastructure (e.g., A/B tests, holdouts) that connects user experience/quality signals directly to product growth Key Performance Indicators (KPIs).