Software Engineer, Applied AI, Model Quality

Google•New York, NY

4d•$174,000 - $252,000

About The Position

Google software engineers develop technologies that help billions of users connect, explore, and interact with information. They work on products that handle information at massive scale and may contribute across areas such as information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design, and mobile. Engineers work on projects critical to Google’s needs and may switch teams or projects as the business evolves, applying versatility, leadership, and a willingness to take on problems across the full stack. As part of Google Pics, an AI-powered visual editor integrated across Google Workspace, this role supports generative AI photo and design capabilities directly on the canvas. The engineer will work with the image-understanding capabilities of GemPix and Gemini to help users edit, transform, and generate images across Workspace surfaces. The work involves executing a product roadmap built on limited testing and consumer experiments, and delivering against a roadmap of model capabilities. The role focuses on model quality evaluation: building human-powered and LLM-powered automated evaluation systems, establishing metrics, running evaluations across models and datasets, and using results to guide improvements in collaboration with research and cross-functional teams. Individual pay is determined by job-related skills, experience, and relevant education or training. The stated US compensation is $174,000–$252,000, plus a 15% bonus target, equity, and benefits.

Requirements

Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience testing, maintaining, or launching software products.
1 year of experience with software design and architecture.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
5 years of experience in one or more of the following: speech/audio, reinforcement learning, ML infrastructure, or another ML specialization.
5 years of experience with ML design and ML infrastructure, such as model deployment, model evaluation, data processing, debugging, or fine-tuning.
3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects.
Experience integrating generative AI tools or Large Language Model (LLM) interfaces into workflows.

Responsibilities

Build human-powered and Large Language Model (LLM)-powered automated evaluation systems to assess model performance.
Establish clear metrics to measure grounding, coherence, safety, and helpfulness.
Use platforms and tools to efficiently run evaluations across different models and datasets.
Provide actionable insights from evaluations to improve model quality, often in collaboration with research and cross-functional teams.
Create tools and systems that make the evaluation process more efficient and effective.